Testing Mostly Automated Trofeo Alcudia Preview

Behind a lot of the work on this blog are a bunch of machine learning models which aim to predict elements of cycling races. I predict whether a race will end in a bunch sprint, whether the morning breakaway will win, which climbs will be most impactful, what the winning power efforts will be over 5 minutes and 40 minutes, which riders have the best chance of finishing highly, and roughly what those win probabilities are by rider. None of this stuff is crazy sophisticated, but the outputs are interesting to look at prior to a race.

With that in mind, I’ve setup a script to collate all of this data for interesting races each week and spit-out automated predictions for a range of models. If this experiment continues, I’ll have the script generate a post each evening and if time permits add a bit of commentary around the data. If not, you’ll just get data and charts.

Race Profile

Alcudia will have a single fairly benign climb in the last third of the race before a downhill drop to the flat finish.

Race Features

The models predict a small chance of the morning breakaway winning – which seems very unlikely to me – and good chance for a bunch sprint (20+ riders finishing together in leading group – which seems low considering last year was a sprint. We’ll see if any riders mix it up on the climb.

The predicted efforts by leading riders are 6.1 watts/kg & 420 absolute watts for 5 minutes and 4.3 watts/kg for 40 minutes. For 5 minute efforts, those are slightly above-average for pro races. For 40 minute effort, that is bang on average.

Race Favorites

These are explicitly generated without looking at betting markets which are very likely are generating better predictions. In particular, the markets seem much more convinced of a sprint.

Girmay won last year and is the favorite again. Presumably he will win in a sprint, though if a smaller group (say 10-20) goes to the line he could easily be in it. The leaders are divided pretty obviously into riders who will win in a sprint (Trentin, Garcia Cortina, Bouhanni, Senechal, Teunissen) and those who will win with a late attack on the climb (Powless, Martin, Konrad).

Historical Power Efforts

Last year’s parcours was very similar with more kilometers. It required fairly benign relative watts (6.2 watts/kg for 5 minutes and 5.4 watts/kg for 20 minutes) and larger absolute watts (430 watts for 5 minutes and 375 watts for 20 minutes). The Z-scores compare to all other pro races where larger watts were required for the longer efforts with fairly high absolute power required for 20 and 40 minutes. This race was fairly high intensity at the end in 2022.

Considering the profile is similar, we can look at where peak efforts occurred for 30 seconds and 2/5/10/20/40 minutes for four groups of riders (top 20 finishers, 21-60, 61+, and non-finishers). The race last year was 174 kilometers with the Sa Batalla climb at 138 kilometers.

Unsurprisingly for a race without much going on the main efforts from 5+ minutes happened on the climb. Top finishers did their strongest 30 second and 2 minute efforts at the end.

The Sa Batalla climb was raced with median 5.5 watts/kg and 371 watts in 2022 race with little difference between those who finished in final group (5.51 watts/kg) and not (5.39 watts/kg).

2023 Team Projections

Last year I produced my team-level projections for professional cycling for the first time. In retrospect they were very amateur, being derived solely from rider-level projections adjusted for aging, but which considered a rider to basically be as good in 2022 as they were in 2021 (with some regression to the mean). Obviously that’s wrong. Riders definitely have seasons with positive or negative variance in terms of results – especially with point scales which decline aggressively from 1st to 2nd or 2nd to 3rd place. We must look at longer term data to make the best predictions.

Overall, the projections performed as mediocrely as you would expect, failing to identify Intermarche as a potential breakout team and unable to point to Quick Step struggling (though my model narrowly had them finishing out of 1st place – but they ended up 7th far off the pace!).

Picking EF to finish 8th when they finished 16th was a sizeable miss, as was missing the emergence of Arkea and Alpecin where I underrated both. On the bright side, I identified BORA – hansgrohe as the team with the 2nd best transfer business and their new riders absolutely crushed with Vlasov finishing 5th, Higuita 18th, and Hindley 19th in PCS points in 2022.

Going into 2023

For 2023 I rebuilt everything. This time I generated individual rider projections based on two other projections: 1) how many points a rider will earn per raceday and 2) how many racedays they will compete in. Feeding into those projections are data points from the last three seasons (2020-22) along with their age, a dummy variable for the covid year in 2020, and what level a rider is competing at (World Tour, Pro Tour, etc).

When predicting points earned per raceday, the model weights the last three years as:

Last Three Years Average = 0.20
Last Two Years Average = 0.26
Last Year Average = 0.54

When predicting racedays, the model regresses much more heavily to the mean and looks more strongly at the last two years of data – while projecting most World Tour riders for at least 30 racedays.

The model is still quite conservative – relying heavily on previous seasons of data with small affects for age and level being raced at. This isn’t the type of model which is going to identifying a massive breakout by a neo-pro like Magnus Sheffield had in 2022; the goal is instead to set very reasonable benchmarks for all riders by smoothing out unrealistically strong 2022 seasons, while giving historically strong riders who were weaker/injured in 2022 a little extra credit.

I also don’t do anything special with projecting riders for a certain schedule. Riders transferring to teams who heavily race French / Belgian one day circuits are probably more likely to rack up points (vice-versa for those transferring out of those squads). The opposite is true for teams who focus strongly on just World Tour races like EF. I also haven’t factored in changes in team schedules; Uno-X in particular should be racing a stronger level of race which could lead to more points.

The Top Riders

Given the conservatism of the model, it’s no surprise that the top four riders from 2022 (Pogacar, Van Aert, Evenepoel, and Vingegaard) are the top four riders projected for 2023. These riders could account for multiple grand tours and multiple monuments in 2023. Each has a strong multi-year track record (even Evenepoel still ranked 17th and 18th in PCS points in 2021 and 2020). None of them did anything in 2022 that was unsustainable either.

Below that top four, Roglic, Van Der Poel, Vlasov, and Philipsen are the next tier of riders. While Vlasov and Philipsen are both top 10 riders from 2022, Roglic and Van Der Poel had down seasons due to injuries/crashes/DNFs. Van Der Poel seems to be focusing less on stage racing in 2022 which should help him earn additional points (though the model does not know this!).

Ninth place is where the model stumbles for the first time in my opinion. Adam Yates has a lot of strong racing in his pedigree for 2020-22, but the model doesn’t explicitly know he’s moving to a team with multiple established GC leaders. I’ve tried to incorporate forward looking projections of whether a team has too many leaders, but couldn’t get anything significant to pop out. The devils advocate in me would highlight Yates is coming from a team where he already had loads of competition for GC leadership and perhaps moving to UAE will allow him to target more one day races and non-Pogacar weeklong races.

Rounding out the top 12 riders are Richard Carapaz (moving to EF and certainly will have more obvious opportunities to ride for himself), Mads Pedersen, and Arnaud De Lie.

Largest Improvements and Declines

Among Pro Tour/World Tour riders who were ranked in the top 500 riders in 2022, the largest projected declines come from older riders who over-achieved in 2022. These are riders like Julien Simon on Total Energies – who ranked in the top 75 riders in 2022 at 37 years old – and Alexander Kristoff – who ranked 11th at age 35. Others like Louis Meintjes, Bob Jungels, and Benjamin Thomas had seasons out-of-line with their recent performance and in many cases are on the wrong side of 30. The largest expected declines are something like losing 33% of points so a rider who earned 600 PCS points in 2022 would be projected for about 400 points in 2023.

On the positive side, riders who were impacted by injury in 2022 are projected to return to strength. These are guys like Maximilian Schachmann, Kasper Asgreen, Julian Alaphilippe, Nacer Bouhanni, and Jack Haig. Alaphilippe scored about 28 points per raceday in 2020-21 which is similar to what the non-Van Aert/Pogacar top riders scored. I have him projected for just 14 PCS points per raceday in 2023 which ranks 28th best among all riders – still an improvement over 2022, but below his top seasons.

Team Level Projections

With a decent baseline established for each rider, we can aggregate their expected points into an overall team projection.

Below I have aggregated projections for the World Tour teams plus the four most significant Pro teams in the peloton. These projections assume Astana completes the transfer for Cavendish/Bol; if they don’t Lutsenko projects as their best rider and Astana would fall very close to Uno-X in overall team projections.

I’ve included the delta vs 2022, the team’s projected top rider, and a calculation of the team’s Gini Index (basically a larger number mean fewer riders are projected to gain a larger share of that team’s points).

The theme of these projections are:

  1. The rich only get richer – UAE and Jumbo project to improve by the most and 3rd most due to leveraging strong transfers and additional young talent.
  2. Quick Step is projected to return from 7th best to 3rd best in the peloton.
  3. Intermarche is projected to decline significantly. Most interesting for them is that they have by far the lowest Gini index among these top teams meaning they have great depth to still be able rank in the middle of the pack, but lack a clear massively productive rider. Perhaps Girmay takes another step forward this year.
  4. Lotto-Dstny is incredibly reliant on De Lie and to a lesser extent Ewan to generate their points. Those two are responsible for approximately 35% of their projected points. They’ve shed four of the top 10 riders from 2022 from a team which was already in the bottom third.
  5. A different model loves EF-Education! To be fair, adding Carapaz is a major addition of the projected 10th best rider. Honore is also projected to improve vs 2022 and they have a full season of Piccolo.
  6. INEOS might have the most interesting projection; they look like they are losing a lot of talent given Yates, Carapaz, and Van Baarle are out and only Arensman is in, but there is a load of young talent which could be given more responsibility. If I had to pick a team to overperform their projection they would be it.

I also ran the numbers specifically looking at team’s aggregates for top 20 riders vs all their riders with the new UCI rule changes to count UCI points for the top 20 riders instead of top 10 in the relegation battle. The results were uninteresting with no team ranking more than two spots different (Team DSM 18th on all riders and 20th on the top 20 riders).

Finally, I wanted to understand which teams had age on their side. I could take a straight average of rider age, but that counts neo pros who aren’t going to race a ton or make a huge impact the same as a prime age superstar rider. Instead, I just weighted a rider’s age by their predicted points. This produced a very tight distribution of quality adjusted age, but the high and low teams are interesting.

Lotto-Dstny, Team DSM, and Trek have the four lowest quality adjusted ages among these top teams at between 26.7 and 27.0. INEOS ranks fourth best at 27.2 – below even Uno-X. The clear oldest is Israel Premier Tech at 32.6. Astana, TotalEnergies, and Bahrain are the next three oldest at over 30.

Evaluating Quick Step’s Dominant Transfer Performance

Top-level professional cycling teams typically turnover around 20-25% of their roster (~7-8 of ~30 riders) year-over-year. Along with organically developing talent, in and out transfers are the primary way teams can change their level of performance. By consistently acquiring better talent than they part with – and particularly talent which fits into their team and which they have a plan to develop, a team can aim for promotion to the World Tour and compete for monuments and grand tours. A team which consistently brings in inferior performers to what they send out will likely struggle to perform and retain sponsorship over the long-term.

Typically, transfer quality has been evaluated immediately prior to a new season by judging the quality of riders brought in (measured perhaps by UCI or PCS points) minus the riders lost. This method works as a rough accounting of riders in and out, but such a method fails to adequately judge transfers by 1) focusing only on the just-finished season performance (ie, a rider is considers to be their most recent body of work) and 2) not accounting for actual performance of the rider (which is subject to impact of age, new training methods, health, race schedule, and ultimately randomness, but is still an important factor to judging transfers).

For example, in the above link Quick Step going into 2022 ranked 18th of 18 World Tour teams in PCS points surplus for riders in vs riders out. They lost Almeida and Bennett, while signing a handful of younger riders. In the end, their five new young riders scored nearly 1000 PCS points (almost +800 vs 2020-21 average), while the riders they lost scored only 1600 PCS points (a drop of -600 vs 2020-21 average). Ultimately, based on improvements by riders coming in and declines by riders going out, Quick Step probably had a top three transfer performance among World Tour teams.


I introduce a new evaluation system which measures the change in PCS points won by riders in year N + 1 as compared to years N – 1 and N, while also looking at the source of a transfer (Continental team, junior/national/club team, or Pro/World Tour level team).

By this method, Quick Step stands out as the superior World Tour team in the period of N = 2012 to N = 2021 on average improving the output of the riders acquired by 81% (eg, an average of 1000 points in years N – 1 and N = 1810 points in year N + 1) compared to a range of -11% to +38% among other World Tour teams.

Just looking at transfers brought in from established professional teams Quick Step also leads with an average improvement of 49% year-over-year compared to a range of -22% to +22% among other teams. Quick Step has signed riders who produced an average of 346 PCS points vs 514 PCS points in their first season with Quick Step. The trailer in this regard is Cofidis who have signed riders with an average of 246 PCS points who have produced 192 PCS points in the first season with the team.

Quick Step isn’t necessarily shopping at the top of the market either given that their average PCS points per signing rank just 9th highest (about average) among World Tour teams at 346 per signing (just looking at riders signed from other pro teams). UAE Team Emirates ranks by far the highest with 446 points per signing while Intermarche – Wanty shops at the bottom of the market with 174 points per signing.

Teams don’t just sign riders from other professional teams; they have to identify and convince top juniors and U23 riders to join them directly or join the development team with a path to the World Tour. Looking at teams who sign riders from all non-professional sources (Conti level, juniors, national teams, club teams), Quick Step again stands out with acquired riders producing about six times (+521%) the average points they produced in the two previous years. This compares to a range of +19% to +490% for World Tour level teams. These massive gains are because PCS points scales aren’t designed to fully reward races below professional levels (.2, U23, Junior, and Nations Cup races) and riders race more often in World Tour.

Since 2012, Quick Step has three of the eight best neo-pro transfers with Evenepoel in 2019, Gaviria in 2016, and Jakobsen in 2018.

The simplest way to illustrate Quick Step’s transfer success is to just look at the percentage of signings who score more PCS points in the next year for signings from other professional teams; across the World Tour that value is 40% for all signings. Quick Step manages to improve 60% of their signings – only BORA – hansgrohe crosses the 50% success mark.

Again, we can simply explain Quick Step’s success at signing non-professional riders by calculating the percentage of transfers who score 100+ PCS points in the next season (this is roughly the level of the 500th best rider in the pro peloton). Quick Step manages to get 65% of these transfers to this level compared to just 32% of riders on other World Tour teams. Only three World Tour teams manage even 50% of riders scoring 100+ points.


Quick Step is also well-known as a team which knows when to allow a rider to leave (after a career year will make them too expensive or when age will cause their performance to decline). I repeated the same analysis for teams looking at how riders who left the team performed in the year after (N + 1) compared to an average of the two years prior (N & N – 1).

Again, Quick Step has distinguished themselves by ranking third best over this time period among current World Tour teams with their riders declining by -24%, behind Intermarche at -29% and BORA – hansgrohe at -27%. The trailers in this category (meaning riders leave and win more points) are Alpecin – Deceuninck (+39%) and Cofidis (+14%). Alpecin has had comparatively few and typically unproductive riders leave which might skew the numbers.

Quick Step has four of the seven riders who transferred out and lost the most PCS points dating back to 2012 – Elia Viviani (Cofidis in 2020), Marcel Kittel (Katusha in 2018), Philippe Gilbert (Lotto in 2020), and Michal Kwiatkowski (Sky in 2016).


Riders who transfer in after injury or another less typical situation might artificially inflate certain team’s transfer successes. For example, it’s not immediately clear how much of Mark Cavendish’s relative success in 2021 vs 2019-20 was due to 1) Quick Step’s unique team situation, 2) improved health/ability to train, 3) dumb luck, or some other factor. In this analysis, Quick Step gets the credit for identifying his talent, convincing him to join them, and executing on returning him to greatness.

This approach also only considers the immediate N + 1 season where many riders are signed for 2+ seasons. Young riders particularly may be signed with an eye towards development by certain teams with the idea that they will pay-off in N + 2 or further seasons. Not considering contract length and performance over a contract is a limitation.

None of this analysis considers salary. A rider who produces less value than in previous years, but who was signed to a cheaper than expected contract could be a successful transfer. A rider who improves year-over-year, but was signed to a more speculative high-price contract may be an unsuccessful transfer. Without knowledge of salary it’s always going to be tough to evaluate transfer success. However, a very rough proxy for salary is a riders recent performance. A rider who scores 1000 PCS points and then signs a new contract will likely be paid more than a rider who scores 250 PCS points and then signs a new contract. Thus in the aggregate, we can probably judge a team who consistently signs a rider scoring 1000 points who then scores 800 points the next year as getting poor value.

Also, none of this analysis considers need or role. For example, a team which is desperate for a sprinter to fit in their team might be justified paying for a sprinter who is likely to underperform their prior seasons of performance. Also, a team with a GC captain, but lesser support riders, might be justified signing a few support riders who had bigger roles elsewhere knowing their output will decline, but on aggregate the team will achieve more.

Stage 6 Tour de France Preview

This is a very interesting hilly stage which has the potential to turn out in a bunch of different directions after a start to the Tour which has pretty much followed the script so far.

Headline Numbers

These are outputs of machine learning models trained on top-level races in the last six years.

Climbing difficulty: 5.4 (on a scale where 0 = flat and 20+ = high mountains)

Probability of morning break winning: 32%

Probability of ending in bunch sprint: 8% (at least 20+ riders finishing together in winning group)

And 220 kilometers in length, which is the longest of this year’s Tour

Similar Stages

Recent similar stages in terms of parcours and uphill finish include Stages 1 and 2 in 2021, Stage 3 in 2019, Stages 5 and 6 in 2018, Stage 3 in 2017, and Stage 2 in 2016. Each had hilly parcours without any significant climbing efforts, but ending in a fairly short uphill finish. Those seven stages have been won 3x by Sagan, 2x by Alaphilippe, and once by MVDP (with Dan Martin winning also). So these stages tend to be won by ultra-elite punchy riders.

However, the yellow jersey was in play at least hypothetically for a bunch of riders entering those stages which kept their teams engaged in A) limiting who got away in morning breakaway and B) trying to pull that break back. In those seven stages, between 4 and 7 riders got into the morning breakaways and the groups were mostly riders from second tier teams with 2019 Tim Wellens being the best of the bunch.

In this case, only four other riders are within 30 seconds of Wout Van Aert. Of them, Lampaert and Boasson Hagen don’t have a chance of taking the jersey from Van Aert from the peloton tomorrow. Powless perhaps has a very outside shot. Pogacar probably has no interest with the efforts on the cobbles yesterday and a mountain top finish tomorrow. That leaves no team particularly interested to chase to try to get into yellow.

In terms of the stage win, this is not a finish for sprinters. Of the seven comparison stages, they were contested by final groups of 40 or fewer riders where Sagan, Michael Matthews, and Sonny Colbrelli were the most “sprinter-like” riders in the bunch. Translating that to 2022, that means Ewan, Groenewegen, Philipsen, and Jakobsen probably have no shot tomorrow. That means four fewer teams who will be interested in chasing things down; rather, they’ll want to fire riders up the road in the breakaway.

That leaves almost every team outside the six with GC favorites (UAE, Jumbo, INEOS, Movistar, Bora, and FDJ) interested in making sure they are represented in the breakaway. In the first week of stages in the last four Tours it has been extremely rare for double-digit riders to get into the morning breakaway. We’ve seen it only twice, once on Stage 6 in 2019 (summit finish) and once on Stage 7 in 2021. It is rare to see large breakaways go in the first week, but this could be the day.

How it could play out

I would amend the model’s estimate of the breakaway’s win probability upward to something like 80%. That remaining 20% is if Jumbo-Visma stubbornly brings the breakaway back to keep Van Aert in yellow/try to get another stage win or if something weird happens with morning break and a lot of strong teams don’t get someone in it.

Stage 7 from last year might be the template for how the stage goes. That was also the longest stage in the race after a first week where two hill top finishes and a time trial established a hierarchy. It was hillier with more climbing and less climbing at the very end of the race, but not dramatically so. In the end, nearly 30 riders got away in the first 50 kilometers of the stage including major one day race/classics/puncheurs like Asgreen, Van Der Poel, Van Aert, Mohoric, Stuyven, and Kragh Andersen. The GC group motored in five minutes down still 30 riders strong.

My model which is designed to predict which riders will get in breakaway based on tomorrow’s parcours predicts the following chances (trained on past breakaways so this considers both desire and ability to get into the break). All together, this model predicts 9 riders. So if we see something like 18 riders, we could multiply these chances by 2x. I’ve italicized anyone probably on team duties.

15%: Wellens, MVDP
14%: Gougeard
13%: Bonnamour
12%: Cort, Politt, Perez, Gallopin
11%: Storer, Clarke, Dewulf
10%: Rolland, Teuns, Oliveira, Bouchard
9%: Van Moer, Lafay, Mohoric, Soler, Gilbert, Simmons
8%: Goossens, Tratnik, Mollema, Sanchez, Houle, Van Der Hoorn, Benoot, Turgis, Martin, Kamna, Woods, Dillier

Notably on this list there isn’t a Quick Step rider as Cattaneo, Honore, Bagioli, and Asgreen are all 6-7%. Surely they will be represented in any move which has a chance to stick.

In addition, it’s possible we see non-traditional breakaway specialists try to get into the move. This could be someone like Sagan, Michael Matthews, or even Jasper Stuyven if he gets any freedom from the peloton.

Combining this intuition about break’s chances with support from the numbers I have the following probabilities for tomorrow given an 80% chance of morning breakaway winning:

RiderWin Probability
Van Der Poel Mathieu21.2%
Wellens Tim4.2%
Mohoric Matej4.0%
Woods Michael3.5%
Matthews Michael3.1%
Bonnamour Franck2.5%
Teuns Dylan2.1%
Pogacar Tadej1.9%
Roglic Primoz1.9%
Van Aert Wout1.9%
Martin Guillaume1.9%
Stuyven Jasper1.8%
Mollema Bauke1.8%
Ciccone Giulio1.6%
Vuillermoz Alexis1.6%
Asgreen Kasper1.6%
Bagioli Andrea1.6%
Lutsenko Alexey1.5%
Honore Mikkel Frolich1.3%
Barguil Warren1.2%
Guerreiro Ruben1.1%
Sagan Peter1.0%
Kron Andreas1.0%
Bardet Romain0.9%

MVDP at 21% is pretty rich given his mediocre form so far, but given his overall ability + propensity to get into breakaways I can make sense of it. I doubt Bonnamour at 2.5% to get his first pro win today is close to right.

GC in 2022 Tour de France

There’s a number of big stories on the general classification of this year’s race. Obviously Pogacar is going for his third straight Tour all before turning 24. He’ll get another showdown with Primoz Roglic after their tight battle in 2020 and Roglic crashing out of the race in 2021. Jumbo-Visma also has another contender beyond Roglic in the form of last year’s 2nd place Jonas Vingegaard. And a bit under-the-radar, but INEOS Grenadiers haven’t gone more than three grand tours without winning GC since 2015 (a time period during which they’ve won 9 of 21 GC titles).

Pogacar vs Roglic?

Pogacar is the bookmakers favorite – significantly – with a price of about 1.77 for/1.90 against at Pinnacle (implied about 52% to win the Tour). Both Roglic and Vingegaard are implied around 12-14% based on odds of about 5.0. Vingegaard has shortened significantly since a strong performance in the warmup Dauphine race as Roglic used to have roughly two thirds of the win probability among the two in March, but that has shortened to about 50/50 or even advantage Vingegaard in the last two weeks. So while this race will certainly be billed as Roglic vs Pogacar, Vingegaard is coming in very strong to have equal odds – especially given the team will certainly defer to Roglic a bit.

What percentage of Roglic+Vingegaard win probability does Roglic have?

Of course the question must be asked whether Pogacar deserves to be the massive favorite? He is likely the biggest Tour de France pre-race favorite since Chris Froome in 2013 (I’ve seen about equal odds for Froome 2013 as Pogacar this year). In fact, that’s still true looking at all grand tours back to that 2013 TDF, so Pogacar’s as big a grand tour favorite as we’ve seen in 26 straight races.

If you average each rider’s top 7 GC performances in the last three years – using Pro Cycling Stats points system – Roglic and Pogacar come out well ahead of the competition, but close to one another. The 300+ points accrued by this scale is approximately what Roglic entered last year’s race at and similar to where Chris Froome entered the 2017 edition. Froome entered the 2018 Tour at over 400 points on this scale.

RiderAverage PCS Points of Top 7 GC Performances
Primoz Roglic333
Tadej Pogacar306
Geraint Thomas202
Adam Yates189
Enric Mas184
Jonas Vingegaard179
Top 6 riders in 2022 TDF by this method

In fact, whatever way you slice and dice it, Roglic and Pogacar have gained points in GC races at nearly identical rates. So why is Pogacar such a massive favorite?

My hypothesis is that Pogacar has shown himself more capable of putting out truly dominant performances. I’ve generated a quick method to find the most dominant stage race performances in recent years. What I’ve done is strip out riders in the breakaway, and then take the average seconds gained over other top riders in the stage. We’re trying to identify performances like Chris Froome’s multi-mountain raid in Stage 19 of the 2018 Giro where he won by 180 seconds over 2nd place.

Indeed, among grand tour stages since 2018, Froome’s victory in that stage rates #1 with a weighted average of 273 seconds over the chasers. Pogacar’s Stage 8 victory in last year’s Tour ranks #2, and Richard Carapaz on Stage 14 of 2019 Giro ranks #3. That’s a pretty good short-list of dominating efforts – basically multi-climb mountain raids.

Expanding out from grand tours, Pogacar also has >60 seconds gained dominant performances on Stage 6 of 2022 Tirreno Adriatico, Stage 20 of 2019 Vuelta, the Stage 20 Time Trial in 2020 Tour de France, Stage 5 of 2021 Tirreno Adriatico, and Stage 9 of 2019 Vuelta. That’s a total of six massive efforts in three years which won him four races and produced his shock podium at 2019 Vuelta.

In the same time period, Roglic has just three similar efforts – all in the final week of the 2021 Vuelta on Stages 17, 20, and 21 (and that Stage 20 effort was a group effort with other riders). Roglic just has not shown the ability to produce massive race-winning efforts nearly as often as Pogacar relying more on a very strong time trial and late attacks on climbs.

Of course, looking physiologically it’s also possible Pogacar just has more watts available for longer than Roglic. CronosWatts.com produced a phenomenal article comparing the two riders over their careers back in March, looking at their best climbing performances and the times and watts per KG they produced. I’ve overlayed their two graphs showing the two riders with Roglic represented by the green trend line and Pogacar in blue.

Frederic Portoleau’s conclusion:

The 2 Slovenians have a very similar level in the mountains for durations of effort of less than 25 minutes. For the long climbs, a small advantage for Pogacar. On a climb like Alpe d’Huez Pogacar must be able to achieve a time of 38min30sec or a little less in the event of maximum effort. Roglic for his part, has the potential to climb Alpe d’Huez in 39 min.

Frederic Portoleau from https://www.chronoswatts.com/news/203/

Perhaps you can argue Roglic is faster on the sub 10 minute climbs which might allow him to steal some time on the finishes of stages 6, 8, 9, and 14, but overall they are even on efforts like those faced on Planche de Belles Filles on Stage 7 and Peyragudes on Stage 17. Pogacar has the advantage on climbs like Col du Granon (Stage 11), Alpe d’Huez (Stage 12), and Hautacum (Stage 18) – at least using these historical values.

The same site has published summaries of 2020 and 2021 Tours with major climbs. Unfortunately they don’t include the Planche de Belles Filles time trial in 2020 or the full Ventoux ascent where Pogacar was dropped by Vingegaard and lost 40 seconds in 2021. They also include non-competitive climbs like Stages 6/16 in 2020 where GC riders were not riding full gas. Looking at the summary Average Standard Watts Pogacar beat out Roglic by maybe 0.5% in 2020 and Vingegaard by about 2% in 2021. The missing data works against us here, but just using their times in lieu of power estimates, Pogacar rode 8% faster than Roglic on Planche de Belles Filles and Vingegaard rode full Ventoux ascent 1% faster than Pogacar. Combining those values with other climbs says Pogacar has been about 1.5% better over the last two Tours. That seems like enough to call him a clear favorite.


The Pogacar vs Jumbo-Visma battle won’t just be confined to those three riders; depending on tactics we could see one of Jumbo-Visma, UAE Team Emirates, or even INEOS Grenadiers try to control the race by leveraging their teams. In fact, we could see this early on potentially windy stages like 2, 3, and 4 or the cobbled stage 5.

I’ve ranked team quality on x-axis of who has the better time trial riders and y-axis of who has the better classics/one day riders. This might give us an indication of who is best setup to support their riders on the flat or hilly days where there is wind or cobbles in play.

Jumbo-Visma is the clear leader here as they have very strong time trial riders supporting Roglic like Van Aert and Vingegaard while also having strong classics riders like Laporte and Benoot. UAE is one of five strong teams behind Jumbo-Visma along with INEOS, Quick Step, and Bahrain, and BORA. Advantage Jumbo-Visma, but this isn’t a chasm like between Jumbo and Movistar.

Moving to the mountains, during the last two Tours there’s been a lot of talk about Pogacar’s team not being strong enough to support him, while Jumbo-Visma has been seen as a super team with multiple GC contenders lining up to support Roglic. Measuring a climbing domestique’s ability to support GC riders is still definitely not a solved problem, but I’ve tried leveraging my rider ratings which identify how good riders are at racing certain parcours based on their finishing position. Lower values below indicate better expected finish positions across the top 4 support riders on each squad.

TeamAverage of #2-5 ClimbersTeam Climbing Rank
2020 Jumbo-Visma12.41st
2020 UAE15.64th
2020 INEOS15.85th
2021 Jumbo-Visma11.83rd
2021 INEOS11.01st
2021 UAE16.54th
2022 Jumbo-Visma9.71st
2022 UAE12.82nd
2022 INEOS15.54th
Expected climbing performance rank by top 4 climbing domestiques

In 2020, Jumbo-Visma had by far the best climbing domestiques to back up Roglic and they rode a defensive race which delivered Roglic to the final time trial with a minute advantage. Roglic’s teammates could only watch as Pogacar made up the difference and won the Tour.

Last year, Jumbo-Visma again had a wide advantage over UAE, though INEOS was strongest, but a strong team was less important after Pogacar’s incredible first week and the team had an easy job to protect a five minute lead after nine stages.

This year, Jumbo-Visma will again have an advantage over UAE, but only because of how much stronger their lineup is this year. Both squads have improved vs 2020 and 2021. Sepp Kuss is likely the best pure climbing domestique in the race – ranking 14th in my climber rating – which will allow Jumbo-Visma to have something like three of the final 15 riders in the lead group. UAE added veterans Marc Soler and George Bennett over the offseason which should give Pogacar’s team something like 5 riders in the last 40 riders in the lead group compared to just two in 2021.

Other Contenders

Based on betting odds and making reasonable assumptions about where the vig is on the GC winner market, books are pricing Pogacar, Roglic, and Vingegaard at something like 80% for one of them to win. That leaves about a 20% chance of a big surprise whether from a former winner like Geraint Thomas, a perennial contender like Yates or Quintana, or one of the younger crowd of podium contenders like Vlasov or Enric Mas.

Contenders other than Roglic, Pogacar, JonasImplied Probability of Winning
Geraint Thomas3%
Daniel Felipe Martinez2.5%
Aleksandr Vlasov2%
Ben O’Connor1.5%
Enric Mas1%
Jack Haig, Damiano Caruso, Adam Yates<1%
Jakob Fuglsang, Romain Bardet, Nairo Quintana<1%
Alexey Lutsenko, David Gaudu, Rigoberto Uran<1%

This gives INEOS perhaps a 6-7% chance of winning their first Tour in three years. Thomas has just won the Tour de Suisse – one of two big warm-up races, but only after the favorite Vlasov left with a positive Covid test. Martinez had an incredible spring with a win in the Tour of the Basque Country and podiums after two other big races, but looked undercooked at the Suisse warmup and has a best grand tour GC result of just 5th. The final INEOS rider Adam Yates ranks as the third best performing rider on climbing stages in the race, but has just two 4th places in his GC career largely due to a poor time trial and big drop-off in performance in later stages of races.

Of the remaining riders, Mas, O’Connor, and Haig will likely be done in by the 40km time trial on stage 20 where they could easily lose two minutes plus to the Slovenians/Vingegaard. O’Connor was the final rider dropped by Roglic/Vingegaard on the final stage of the Dauphine tune-up race and while he finished 4th last year, he benefitted from gaining 6.5 minutes on other GC riders in a breakaway and likely wasn’t the 4th best rider in the race.

Vlasov has a string of strong week-long GC performances in the spring including a massive win in the Tour de Romandie mountain time trial, but he left Tour de Suisse with Covid. If he’s back on form he has a decent podium chance as his team support ranks 3rd best in the mountains with a strong squad of Austrian/German climbers.

Sprinters in 2022 Tour de France

One of the biggest stories leading into the 2022 Tour de France was whether Quick Step will select young sprinting star Fabio Jakobsen or 34-time Tour de France stage winner Mark Cavendish to lead them on sprint stages. That was resolved today with Jakobsen’s selection. That significantly clears up what should be a very compelling sprint battle between two young stars – Jakobsen and Alpecin’s Jasper Philipsen – and a host of veterans including Caleb Ewan, Dylan Groenewegen, Peter Sagan, and Wout van Aert.

Who are the best sprinters?

I’ve written this year about my Bunch Sprint Model which evaluates sprinting success based on finishing position solely in sprints a rider contests while also considering the strength of opposition a riders sprints against again considering only those opposing sprinters who contested the sprint. You can read more about methodology and results at this link. Think of this model as looking to identify the best sprinters if they all had a chance to sprint against one another.

Back in February this model loved Fabio Jakobsen due to a very high hit rate in sprints he actually contested. Since then, Jakobsen has continued to sprint at a high level with six wins on a variety of parcours, while Jasper Philipsen performed very well at UAE Tour and then hasn’t done much since. This model evaluates the two of them as neck-and-neck on top of the sprinting world.

Behind them, the model rates Cavendish 3rd and Olav Kooij 4th. Cavendish failed to be selected, while Kooij also couldn’t rate selection on Jumbo-Visma’s GC focused squad. Caleb Ewan (5th) and Wout Van Aert (6th) are the other two elite sprinters at this year’s Tour. Van Aert has only participated in a bunch sprint seven times in 2022, but six have been podiums.

Further down the list are veterans like Alexander Kristoff, Dylan Groenewegen, Peter Sagan, and Mads Pedersen. Kristoff landed Stage 1 and the yellow jersey in 2020, but hasn’t won a World Tour sprint since. Sagan has had multiple covid bouts, but finally landed his first win in Tour de Suisse a few weeks ago. Pedersen might be more of factor on the more classics-like finishes as three of his five 2022 victories have come on either uphill finishes or finishes with a small climb right before the finish.

Groenewegen has been a bit in the wilderness due to his suspension, being eclipsed by younger riders at Jumbo Visma, and his subsequent transfer to Bike Exchange. He’s won five times this year, but has only a single podium finish in World Tour sprints. He’s actually won his last three contested sprints across three sub-World Tour races, but was dropped on several climbs in Dauphine and left that race without contesting a sprint.

The only other sprinters it makes sense to mention are Team DSM’s Alberto Dainese and Bike Exchange’s second sprinter Michael Matthews. Dainese won a shock victory in Stage 11 of the Giro, but doesn’t have another finish better than 5th in a sprint all year. Matthews is really more of a tough parcours sprinter at this point in his career as his only wins since 2020 have come in one day classic Bretagne Classic and on a tough stage of the Volta a Catalunya this spring.

Ewan’s Lotto Soudal Team Changes Strategy

Caleb Ewan’s Lotto Soudal team has a well established approach to grand tours since landing Ewan in 2019. They’ve brought the 6th, 2nd, and 2nd heaviest lineups to the last three Tours de France and 4th, 5th, and 4th heaviest to the 2019, 2021, and 2022 Giros – driven by big engines like Roger Kluge, Jasper De Buyst, and Thomas De Gendt. Beyond the size of Ewan’s teammates, they relied on experienced riders to back Ewan, regularly trotting out lineups where 3-4 riders had ridden 25+ bunch sprints with Ewan in recent seasons. That element will be different in 2022 as while they will again have one of the heaviest starting squads, Ewan’s teammates have very little experience supporting him in sprints. Riders like Kluge, De Buyst, and recent additions Michael Schwarzmann and Rudiger Selig were left out in favor of more classics focused engines like Frederik Frison and Florian Vermeersch. It will be interesting to see if their tactics shift more towards Ewan surfing wheels rather than utilizing a big sprint train.

Philipsen’s Rise

Jasper Philipsen was always a highly touted sprinter, landing three World Tour stage wins before his 23rd birthday, but he was squeezed out of UAE Team Emirates by veteran sprinters and the team’s GC focus around Tadej Pogacar and transferred to Alpecin for the 2021 season. There he has blossomed into potentially the best sprinter in the pro peloton thanks to a massive 2021 season.

His 2021 story was inextricably tied to Mark Cavendish as he was Cav’s main opponent in his breakout Tour of Turkey in 2021 (Cav landed four wins to Philipsen’s two) and again he kept coming up short to Cavendish in the Tour de France where Philipsen reeled off six stage podiums, but couldn’t score a win. Philipsen followed up the Tour with two Vuelta stage wins and four one day race wins, including on cobbled terrain. If the cobbled stage 5 turns out to be less vicious than expected it wouldn’t be a surprise to see Philipsen sprinting for the win as he’s handled similar terrain in the past.

Bike Exchange All in For Groenewegen

Before Bike Exchange’s team announcement there was all possibility of them sending a balanced team to chase GC or stages with Simon Yates, but instead they’ve gone all in on Groenewegen and Matthews as of their announced team only Nick Schultz is anything of a climber. They will likely have the heaviest lineup at the Tour at 73.75 kg; that would also be the heaviest of any team at the Tour since Lotto Soudal’s 2016 team built around Andre Greipel and his sprint train. Bike Exchange will hope that extra power will allow them to keep Groenewegen up front during potentially windy stages 2, 3, and 4.

TeamAverage Weight of Riders in KG
Bike Exchange73.8
Lotto Soudal72.6
Quick Step71.5

Fabio Jakobsen + Michael Morkov

Michael Morkov’s dominance as a leadout man has been well established in recent years as he’s guided Sam Bennett and Mark Cavendish to back-to-back green jersey wins and six Tour de France stage wins. Of course combining him with Fabio Jakobsen should produce good results. However their success in 2022 has been massive with Jakobsen winning five of seven sprints he has contested with Morkov in the lineup.

It’s difficult to know which are the best leadout riders on Quick Step as the team is just phenomenally well drilled overall, but it seems like Jakobsen has ridden more with the ‘B’ team than the elites. Since last July when Jakobsen started sprinting again he’s ridden most often with Florian Senechal and Bert Van Lerberghe in bunch sprints. On the other hand, Cavendish has been most often deployed with Morkov and Davide Ballerini.

I’ve written in the past about how tough it is to evaluate sprint helpers and that the best guide may just be to look at how teams deploy riders in different races. With a start in 2022, Morkov will now have raced four straight Tours for Quick Step (2019-22) – as well as the 2022 Giro with Cavendish. Compare that record to Senechal and Van Lerberghe. Senechal has ridden just two grand tours (neither the TDF) in that time period, while Van Lerberghe has similarly raced just two grand tours and won’t feature in this TDF either. While Quick Step’s full sprint train might be a bit lighter than past years, combining Jakobsen with Morkov could still produce tons of success.

Van Aert’s Green Jersey Bid

Van Aert appears to be a massive favorite for the green jersey points competition as his odds – even after a minor injury last week – sit at 1.65 (implied at around 56%). He benefits from a race bereft of many true sprint stages (only four are evaluated in the Tour regulations as flat stages: 2, 3, 19, 21) where true sprinters could challenge him, while there are also a lot of classics-esque and medium mountain stages where he should find great success from a reduced peloton.

YearStages evaluated as flat by organizers

This graph above shows the result of a model which considers whether a rider was able to survive in the group and sprint for the win (finish in top 25 in a bunch sprint race) depending on the climbing difficulty of the parcours. Something like stage 6 into Longwy or stage 8 in Lausanne would rate a 5.5 on this scale, while the flat stages 2 and 3 would rate <1. Stage 10 which features a long, shallow drag to the finish in Megeve would rate just off this scale ~8.5.

Van Aert, Matthews, and Philipsen show fairly consistent ability to stick with the front group as climbing intensifies, but the other sprinters show degraded abilities on tougher terrain – particularly Jakobsen and Groenewegen.

It will be a huge advantage for Van Aert that he has been >85% to survive to the sprint finish regardless of the difficulty of the climbing that day. His ability to survive on those tougher sprint days like stages 4, 6, 8, 13, and 15 and to even get into the break on harder days will make him tough to defeat.

Looking at the last 10 points competitions, we can split up the source of points between finish-line sprints and intermediate sprints. Finish-line sprints can be accrued by being a great bunch sprinter, while intermediate sprints can be accrued by getting breakaways or by tactically out-sprinting opponents on the road. In the last 10 competitions, the green jersey winner ranked 1st in points from finish-line sprints 8 times and 2nd twice. The record on intermediate sprints was more mixed with Kittel, Cavendish, Bennett, and Sagan twice taking green without gathering the most intermediate sprint points. We haven’t recently seen someone take green by dominating intermediate sprints and not being one of the two best on finish-line sprints. Will Van Aert be one of the two best sprinters on finish-line sprints?

If not, the market shows Jakobsen and Philipsen as the best odds among pure sprinters. Jakobsen won the points jersey at 2021 Vuelta and might look to follow Cavendish and Bennett and Quick Step green jersey winners. Philipsen was fairly close to Jakobsen in points competition when he abandoned Vuelta, but he managed only 4th in green jersey race in 2021 Tour de France due to hardly contesting intermediate sprints.

A Better Bunch Sprint Model

I introduced a very basic model for rating riders two months ago which simply took the natural logarithm of finishing rank in each race to make the stat Log Rank. At the end of that piece, I introduced a way to model Log Rank over long time periods to find whether riders a) achieve better or worse finishing ranks overall, b) achieve better or worse ranks in bunch sprint finishes, and c) achieve better or worse ranks in races with a lot of climbing. That ranking model does a good job of distinguishing riders who are expected to perform better or worse in bunch sprints, but not a great job at distinguishing truly great from merely good sprinters.

The issues with that Log Rank model are: 1) it considers all different parcours of races in building the overall impact data point, not just races ending in bunch sprints, 2) it considers all bunch sprints for a rider, even those where a heavier sprinter was jettisoned a climb and failed to participate in the sprint finish, 3) it considers bunch sprints where a rider was present in the bunch, but was actually helping a teammate (eg, Davide Ballerini often sprints for himself in smaller races, but is in the sprint train for bigger ones), and 4) it doesn’t consider the quality of the sprinters participating alongside each rider in the sprint (ie, the competition on that day may be much reduced by tougher parcours, mechanicals, crashes, or splits in the bunch).

So how to account for these issues. First, we want to just evaluate sprinters based on bunch sprint finishes. Anything which doesn’t end in a bunch sprint is ignored by this new model. Second, we want to ignore any race for a rider where they didn’t finish with the first group in the sprint AND in the top 25 positions; this indicates they were capable of sprinting. Third, we want to ignore any race where a rider wasn’t the top finisher on the team. Many riders participating in as a lead-out man can rack up 10th place finishes which can pollute our understanding of them as sprinters in races where they compete as team leader. And fourth, we consider the cumulative strength of the sprinting field which meets these first three criteria based on the simple Log Rank model outputs.

Determing strength of sprinting field

How does point #4 above work in practice? Seventeen sprinters in UAE Tour stage 1 on Sunday qualified for these criteria including the top 13 finishers. My basic Log Rank model predicts following finishing positions in a generic strong race for those seventeen riders.

RiderPredicted Rank
Jasper Philipsen3.0
Arnaud Demare3.2
Sam Bennett3.3
Pascal Ackermann4.0
Dylan Groenewegen4.9
Elia Viviani6.9
Mark Cavendish7.0
Marijn van den Berg9.3
Olav Kooij9.6
Marc Sarreau10.1
Rudy Barbier10.8
Max Kanter16.5
Emils Liepins27.7
Jonathan Milan29.7
Tom Devriendt34.5
Michael Schwarzmann35.2
Jonathan Canaveral47.3
Qualifying sprinters from UAE Tour Stage 1 (2022)

A lot of very talented sprinters were in this race – including seven with an expected finishing rank of 7.0. Compare to stage 1 of Tour of Oman where the top sprinters were Fernando Gaviria (5.0), Mark Cavendish (7.0) and no one else with a predicted rank better than 10.0.

To determine the cumulative strength of sprinting field, I just take the reciprocal of each rider’s predicted log rank (1 / predicted log rank) and add them together. A top sprinter like Bennett or Philipsen will contribute 1/3 or 0.33 points while someone with a very low prediction like Canaveral or Schwarzmann will contibute 1/40 or 0.03 points.

The top races for sprinters tend to be the Tour de France, Milano-Sanremo, Paris-Nice, and UAE Tour with cumulative strength of sprinting fields around 3.0 to 4.0 depending on the specific day. World Tour races in general average just under 2.0, with a wide range, while .Pro races average just above 1.0, again with a wide range. The lowest pro races at .1 level tend to average just below 1.0 with hardly any rating better than 1.5.

With that data calculated, it is simple to specify a model using this strength of sprinting field and rider to predict both finishing rank and whether a rider won the sprint. Both of these models find 1) the impact of individual rider on success metric and 2) a potentially non-linear impact of the cumulative strength of sprint field.

To Predict Finishing Rank:
gam(log(finish_rnk) ~ rider + s(strength_sprint_field))

To Predict Win:
gam(win ~ rider + s(strength_sprint_field))

I ran both models for this example on data since the start of 2020, only considering riders who participated in at least 16 sprints meeting the criteria laid out above. This ranged from Wout Van Aert with 19 sprints to Philipsen/Ackermann with 45.

Who is the top sprinter in early 2022?

Both models produce similar results given the data. Fabio Jakobsen is seen as the most likely sprinter to win a given race and the sprinter who will finish with the best finishing position overall. For example, in a typical World Tour level sprint the models predicts Jakobsen to win 53% of the time and finish an average of 1.9. Wout Van Aert is predicted 2nd in win probability at 44% and 4th in finishing rank at 2.7. Sam Bennett is tied with Caleb Ewan for 3rd in win probability at 38%, but slightly ahead of him for 2nd place in finishing rank at 2.2. Ewan is predicted at 2.6 in finishing rank.

Those four comprise a fairly clear top group with Jakobsen fairly clearly the #1 sprinter in the world. Behind those four are guys like Philipsen, Groenewegen, Cavendish, and Demare. As a sign of his diminished form in recent years, Peter Sagan ranks outside the top 25 in predicted win probability and 15th in predicted finishing rank.

Fabio Jakobsen

Looking at the data in this way it’s obvious why Jakobsen is the top predicted sprinter while ranking only fifth in the PCS Sprinter Ranking and 14th (!) in my own basic Log Rank model. Jakobsen had three week long stage races in his comeback from serious injury last year where he didn’t compete as a sprinter. Basically, the basic Log Rank model sees a guy who was “awful” at sprinting for a dozen sprints. But, when we restrict just to races where he was the team leader and he was in the sprint pack, the graph below shows he has been dominant.

Jakobsen is winning nearly 70% of his sprints where he is the leader and is contesting the sprint since the start of 2020. That blows everyone else away, with Van Aert and Ewan managing only a mid 40% win rate in that time. Jakobsen has raced lesser competition than guys like Van Aert, Ewan, and Bennett, but he’s dominated that competition.

One of the big stories of this and last cycling season is Mark Cavendish’s return to massive success with Quick Step, including tying the record for career Tour de France stage wins. He has twelve wins since the start of 2021 – including two this season – and easily rates as a top 10 sprinter in the world right now. Because he and Jakobsen race for the same team, only one of them is likely to make the Tour de France team where Quick Step sprinters have been steered to 14 sprint stages in the last five races. Unfortunately for Cavendish, Jakobsen isn’t simply just another top 10 sprinter – he’s the best in the world right now.

Too Many Leaders – Analyzing Team Depth

Pro cycling teams have to juggle a lot of goals: for the season, for a stage race, for an individual race. They also need to juggle ambitions of ~30 riders of varying levels of experience and skill. In most races, teams only ride for a designated leader or maybe 2-3 designated leaders. Based on the parcours and who is performing best, teams decide who are the protected leaders and who will be riding in support in each race.

As we move into a new season, teams have hired new riders and let others go. I had a go at making some basic projections on how teams strengthened or weakened their squad with transfers, age based regression, and natural regression/progression in points earned. One of my caveats in that article was that the projections did not account for team strategies or rider schedules based on transfers. There are only so many leadership positions to go around and teams who hire more leaders are at risk of needing to demote some leaders to support roles in certain races.

In this piece I define “leader” as the top finisher for a team in a race. Of course the top finisher is not always the rider(s) who were designated as the leader at the beginning of the race. However, the top six riders in % of races as leader in 2021 were Nairo Quintana, David Gaudu, Giacomo Nizzolo, Guillaume Martin, Aleksandr Vlasov, and Tadej Pogacar so I think it’s a reasonable proxy.

How This Plays Out For Teams

A quick example, UAE Team Emirates transferred in five major signings who spent at least some races in 2022 as their team’s leader – sprinters Pascal Ackermann and Alvaro Hodeg and climbers Joao Almeida, George Bennett, and Marc Soler. They transferred out four major riders who spent some races as leaders – sprinter Alexander Kristoff, climbers Joe Dombrowski, and David De La Cruz, and puncher Sven Erik Bystrom. Five in, four out. The riders leaving were UAE’s #1 rider on a race day 52 times. The riders coming in were their team’s #1 rider on a race day 76 times. UAE also hired wunderkind climber Juan Ayuso who was the leader in a race – primarily at U23 level – 17 times in 2021. In total, they raced 233 times in 2021 and the riders on their team for 2022 were the #1 rider on their team 288 times – a surplus of 55 races.

We can repeat that same calculation for the other 17 World Tour teams and actually most teams have a surplus; 11 have at least 7% more leaders in their team than 2021 race days, another 5 are within +/- 3%, and only Lotto Soudal (6% fewer) and DSM (13% fewer) aren’t equal or with a surplus. Overall, the surplus is 12% at World Tour and 8% at Pro Tour level. This makes total sense. Teams tend to discard riders who don’t have the capacity to be leaders anymore and hire those that do as a natural progression of the sport. However, some teams are legitimately going to be squeezed for leadership opportunities in 2022 – even if we don’t see Covid related cancellations like the prior two years.

EF Education is probably the most over-subscribed in terms of leaders. They hired riders who were team leaders 102 times in 2021, but got rid of riders who were team leaders just 36 times in 2022 – a surplus of 66 races. Their issues might not be as extreme as represented here as many of their additions come from non-World Tour level teams and/or are developing riders who might need a year before becoming full-fledged leaders. In fact, only 77% of the 2021 leaders on EF came while racing for a World Tour level team (86% is the average for the full World Tour).

All Leaders Aren’t Equal

We need to account for the difference acquiring a leader like Giacomo Nizzolo (who finished 1st on his team 60% of races at World Tour level) and one like Marijn Van Den Berg (moving to aforementioned EF Education) who led his team in 45% of races at U23 level. If we arbitrarily assign a weight of 1x for leaders while riding for World Tour teams, 0.67x for leaders while riding for Pro Tour teams, and 0.33x for any other leaders, we can get a better idea of how much competition there will be for leadership roles. At EF, they now rank third with a surplus of about 18%. The World Tour in general averages a 3% surplus by this method.

Doing that weighting shows BORA and Jumbo Visma as the two with the most competitive leadership competitions. BORA ranked 4th best in adding talent through transfers per ProCyclingStats and 2nd best at adding talent by my projections. They added climbers Aleksandr Vlasov, Jai Hindley, and Sergio Higuita who combined to lead their team 62 times in 2021, and sprinters Danny Van Poppel and Sam Bennett who combined to lead 41 times. Sprints-wise, they should be fine as they’re also losing Peter Sagan and Pascal Ackermann (47 races as leaders) and Van Poppel has also said he’s switching to support Bennett.

Where BORA will see the squeeze is in general classification and hilly/mountain stage leadership. Just filtering to leadership in hilly/mountainous races, BORA rode 94 races in 2021, while their currently employed riders were leaders of their team in such races 146 times! That’s a greater than 50% surplus – far beyond any other World Tour team.

BORA’s 2022 squad with 2021 data from hilly/mountainous races

Flip that around to flatter/classics races and Jumbo Visma looks to be the team with the most issues with too many leaders. Despite moving star sprinter Dylan Groenewegen onwards, they’ve still a tight squeeze. They have a surplus of 36% due to adding Christophe Laporte (punchy sprinter), Tosh Van Der Sande (leadout man), and Tiesj Benoot (classics rider). What looks most likely is that those three will simply sacrifice more of their own ambitions to support Wout Van Aert in classics and young sprinters like David Dekker and Olav Kooij in flatter races.

I wrote in my 2022 team projections piece about DSM’s losses in the transfer market. They were especially hard hit in the climbers/GC riders department where they lost Michael Storer, Jai Hindley, Tiesj Benoot, and Ilan Van Wilder. Those four combined to lead in 34 of DSM’s 102 hilly/mountainous races in 2021 and the other transferred out riders combined for 9 more for a total of 42% of DSM’s races being led by riders leaving the team. They only added a sprinter – John Degenkolb – from a World Tour team, with the rest of their additions coming from lower level squads. Still on the team is Romain Bardet (leader in 63% of hilly/mountainous races he entered), but no one else who led in more than 20% of their hilly/mountainous. In races without Bardet, they’ll be handing out leadership opportunities to their wide array of young climbing talent and hoping for quick development.

Team DSM’s 2022 squad with 2021 data from hilly/mountainous races

Competition for Leadership vs Depth/Optionality

The flip-side of framing this as an issue of too many leaders is that talented riders who were leaders in smaller teams can now move up and support superstars like Van Aert. The team also has cover in case of injury; for Jumbo Visma, if Van Aert suffers an injury their spring classics season isn’t completely ruined as they can plug in competent classics riders like Benoot or Laporte.

BORA just released their preliminary plans for the three grand tours, but they also have the option within those plans to either leave off a rider who is struggling with form or choose to fully back a rider in strong form for GC. Between Buchmann, Vlasov, Kelderman, Hindley, and Schachmann they have riders who have finished 4th in Tour de France, 4th in the Giro, 2nd/3rd in the same Giro, and won a World Tour stage race in back-to-back years. And that ignores Higuita, Konrad, and Kamna who have won grand tour stages in the last three years. There’s definitely option value there in knowing that you can select the best of that bunch for your main focus in major races.

Projected Team Points for 2022

It’s not really the start of sports season if an analyst doesn’t produce projections, so I’ve whipped up some basic points projections for the cycling World Tour and Pro Tour teams.

A few points on methodology:

  1. I’ve used the PCS Points from ProCyclingStats.com at the rider-level to build these.
  2. I’ve built a very basic model for projecting points which only knows what a rider did the previous season, how old they are (age matters!), and whether their team is at World Tour or not. Only riders competing in the following year on a World Tour or Pro Tour (and ProConti for past years) level were modelled. Obviously what happened in 2019 and 2020 is relevant, but I will leave a more advanced model to next season.
  3. All riders with a Pro Tour or World Tour contract as of start of this January were predicted for 2022, with their projected points aggregated to determine the collective points projections for each team.
  4. That’s it. I did nothing to account for 2021 injuries, changes in how riders would be deployed across races, and any #gainz which may have occurred over the off-season. This is certainly wrong as many riders who missed large chunks of 2021 will race full schedules in 2022 (Caleb Ewan, Remco Evenepoel, etc.) and we’re already seeing injuries to riders like Mathieu Van Der Poel which will affect points earned in 2022.

Rider Level Projections

A model which just considers the previous year’s performance + age and level of team will tend to produce projections which closely match the rankings from the previous year. The same top five from 2021 is projected to be the top five in 2022, while young stars like Remco Evenepoel (17th in 2021 to 11th in 2022) and Ethan Hayter (26th in 2021 to 17th in 2022) are projected to improve their ranking. Older riders are projected to decline with Alejandro Valverde (12th to 37th) and Mark Cavendish (22nd to 32nd) being the sharpest expected declines.

Above graph describes how riders tend to retain points from year 1 to year 2. Peak age riders tend to regress about 20% or said another way they retain about 80% of their points the next year. Riders who score highest number of points tend to regress more in year 2, while those scoring closer to zero point in year 1 regress less. Younger riders tend to hold onto their points the most (though even highest point riders here tend to regress more). Older riders fall off significantly with a rider scoring 1000 points in year 1 at age 35 retaining more like 70% of their points in year 2.

However, these rider projections are fairly dumb; a projection system which ignores Mark Cavendish doing nothing for four seasons before resurrecting his career is probably not going to make great specific projections for riders. Where I hope the projections do well is at the aggregate team-level where the errors of predicting 25-30 individual riders can cancel each other out.

Team Level Projections

Based on individual rider projections/performances, I created three different team totals: 1) 2021 points earned by the team, 2) the 2021 points earned by the riders employed for 2022, and 3) the projected 2022 points earned by the riders employed for 2022. This way I can calculate who hired the best new riders vs who lost the best riders vs who has riders most primed to improve or decline. Delta due to rider development shows how riders are expected to earn points differently in 2022 vs 2021 due to age or regression. Delta due to Transfers shows how teams added either better or worse riders based on 2021 points. Eg, EF Education hired better riders based on 2021, while Lotto Soudal hired worse riders. However, Lotto is expected to improve due to age in 2022.

World Tour Team Projections for 2022

Of the 18 World Tour teams, I see EF Education improving the most versus 2021 – primarily due to transfers. They signed four top 200 riders in my PCS Points projections (equivalent to a ~top 10 rider on the average team) including Esteban Chaves (projected as their 2nd best rider in 2022). They have only lost three riders who signed with a Pro Tour or World Tour team – headlined by Sergio Higuita (103rd best rider in 2021).

BORA-Hansgrohe is another who looks set to improve significantly due to incoming transfers. They signed the 26th, 29th, 86th, and 88th best riders in my 2022 projections with Sam Bennett hoping to return to his ‘best sprinter in the world’ form. They also added the aforementioned Higuita and Aleksandr Vlasov. BORA loses two strong riders in Peter Sagan and Pascal Ackermann, but they should come out ahead on aggregate.

Lotto-Soudal and Team DSM should improve primarily from internal development of younger riders. DSM is by far the youngest team in the World Tour but has a lot of the early 20s riders who tend to increase significantly. DSM do have to deal with significant losses due to transfers as they were the hardest hit team in percentage terms. Lotto also has a lot of younger riders and do not have any significant regression candidates as their top scoring rider in 2021 was Tim Wellens at only 65th in PCS Points. Caleb Ewan will also presumably have a healthier season (21st and 11th in 2019-20 PCS Points).

My model also projects Quick Step to not lead the World Tour in PCS Points in 2022 (falling just short of UAE Team Emirates by 200 points). They have led in total points accumulated every year since 2013, but the model sees significant riders lost (Joao Almeida in particular ranked 5th in 2021) and significant decline from its current crop of riders (Cavendish in particular). However, the model doesn’t know Quick Step basically got half a season each out of two very promising young riders in Remco Evenepoel and Fabio Jakobsen. My bet is the Belgians manage to pull off their tenth straight #1 ranking by the end of the year.

Among Pro Tour teams, three teams stood out in 2021: Alpecin Fenix out-earned twelve World Tour teams, while Arkea Samsic and Team TotalEnergies earned points like the weakest World Tour squads. The projections see modest regression for both Alpecin and Arkea driven by regression for their top performers and not particularly strong transfers. Team TotalEnergies added Peter Sagan – once the best rider in the world – and should be improved by 30% due to their quality of transfers, but they also are a quite old team which means their gains will probably be more modest in the end.

Pro Tour Projections for 2022

Among others, Kern Pharma is a very young team which should improve due to aging of their riders. They also signed Hector Carretero from Movistar World Tour team who would’ve ranked third on their team in points in 2021. Along with that, they lose only a single rider from 2021.

Uno-X is a team which the projections aren’t particularly high on, but which may be able to improve in ways the models are ignorant of. They are adding Tobias and Anders Halland Johannsen – two elite U23 riders who finished 1st/2nd (Tobias) and 7th/8th (Anders) in the two major U23 races in France and Italy. The U23 points scales on PCS are probably underweighted relative to the difficulty of those races so the Johannsen’s are better positioned to earn points. Not accounting for new opportunities / lesser opportunities for transferred riders is another blind-spot of my model.

Impact of Aging on Performance

Like all physical competitions, cycling is impacted by aging. Younger riders improve their race craft, get access to better coaching/training, and physically mature. Older riders suffer injuries and physical deterioration and succumb to mental pressures of years spent training, travelling, and competing. Younger riders get faster and smarter. Older riders get slower and more worn-down.

Research on many team sports indicate varied “peak” ages for players between early 20s and 30s for different sports. For example, this Baseball Prospectus piece reviews three different approaches and finds somewhere between 26-28 as peak age for hitters. This CJ Turturo piece examines the impact of aging in NHL hockey and finds age 22 as peak for forwards, age 24 for defenders, and age 27 for goalies (part II of that document). Others in studies quoted by Turturo have found 25 for forwards, 22 for defenders, and 24 for all skaters. In 2013, I found golfers peak in their early 30s, which makes sense as golfer is less of an physically demanding sport compared to baseball or hockey. In a later study, I found different aging curves for different skillsets within golf.

I applied similar methodology to these studies above to identify the aging curve in cycling, from which we can derive a peak age and determine how much we should expect young cyclists to improve and old cyclists to decline. Using the delta method where a rider season is compared to the following rider season identified a peak around 26-27 with riders improving before that age and declining after that age. Using the GAM method where a curve is fit to all rider careers identified 26-28 as the peak with riders improving before those ages and declining after 28. The two methods differ in the steepness of the aging curves; delta method shows a steeper curve of improvement < age 25, while GAM method shows a less steep curve of improvement at those ages and a much sharper decline from age 35 onwards.

Methodology and Data

I gathered PCS points per season (raw total) for each rider between 2010 and 2021. PCS points are awarded for race finishes, GC finishes, and points/mountains jersey finishes. The top points scorers tend to reflect who is considered the top riders, but in my opinion they overweight success in one day races and underweight success in stage races (in the individual stages). Nevertheless, they are a well-accepted and discussed data point which is available consistently going back over a decade.

One thing to consider is accumulating PCS points is part performance and part opportunity. A rider who at age 22 races for a Continental level team as the leader in U23 races and at age 23 races for a World Tour team as a domestique will have fewer opportunities to earn points (though improved performance may cancel that out and there are always freaks like Pogacar and Evenepoel).

I also adjusted points earned in 2020 and 2021 to account for the impact of Coronavirus on races being held. 2020 had 14% fewer points earned and 2021 had 3% fewer points earned than an average season.

Important to note: I am using age on June 30th of that season as the age for that season when binning, but otherwise am using continuous ages relative to that June 30th date. Eg, Peter Sagan (January 1990 DOB) is considered as a discrete age 32 in 2022 (as he will be 32 on June 30th) and a continuous age of 32.4 in 2022 (as he will be 32 and 5 months on June 30th). Some other websites report current age and/or use discrete ages which will make ages look lower.


For the delta method, I simply compared points accumulated by a rider in year 1 to those accumulated by that rider in year 2. I used the rider’s age on June 30 of year to determine the rider’s age for that season. The delta method just measures the change between year 1 and year 2, averages across all riders at that age, and ascribes the total average change to aging. My yearly age samples for seasons in the mid-20s were over 1500 and were over 500 for all seasons between 20-33 and over 100 for all seasons between 19-38.

Riders improved their PCS points from 19-20, 20-21, and 21-22 by an average of 88% (eg, 100 points to 188 points). Age 22-23 and 23-24 earned improvements of an average of 38%, followed by 15% from age 24-25. At that point, performance was fairly steady from 25-26 to 28-29 at between up 5% and down 4%. The peak age seems to be from 26 into 27.

Performance starts declining more significantly as a rider moves into their 30s (an average of -12% down for 29-30, 30-31, 31-32, 32-33, and 33-34). The sharper declines follow that, averaging 25% down from age 34 onwards.

Aging impacts at each age produced by Delta Method


For the GAM method, I built a non-linear model which aims to approximate the average aging curve for the full population of riders across their career. The model is in the form of PCS_PTS ~ s(age) + rider so that the overall model finds the average curve over a career; the rider term allows for the height of the curve to vary between massively successful riders like Froome and Cancellara and lower level riders who have scored few points. I included all seasons where riders were between 19 and 38 years old (the ages for which I had > 100 rider samples) and all riders with 4+ seasons in my 12 year sample (using 4+ or 6+ seasons did not impact results).

The aging curve produced was very similar to the delta method. What differed was that the growth curve for riders at 23-24 and under was also much shallower (average of 30% from 19-20, 20-21, 21-22 instead of the 88% from delta method and average of 15% from 22-23 and 23-24 instead of 38% from delta method). The decline curve was sharper after age 35 with 35-36, 36-37, and 37-38 meaning an average decline of 48% instead of 25% shown by delta method.

Aging curve at each age based on GAM Method

This GAM method graph should be interpreted slightly differently as the average progression for a rider throughout their career. The Delta method graph just shows the average change in points season to season at each age. Notably, older ages feature better riders (eg, age 34 is actually the peak for average PCS Points per season because you’ve filtered down to riders who have aged better than the average rider).

What does this mean for 2022?

Summarizing the results of these two approaches, we can see 1) riders tend to improve in earning PCS Points thru age 24 into age 25, 2) riders tend to earn similar PCS Points from age 25 through age 29, and 3) riders start declining in PCS Points earned from 30 onwards, accelerating from age 32-33 onwards.

Among top riders who are in that age 32-34 range we have sprinters like Elia Viviani, Giacomo Nizzolo, and Matteo Trentin, punchier riders like Diego Ulissi and Ion Izagirre, and climbers like Mikel Landa, Primoz Roglic, and Rafal Majka. The most prominent rider who switched teams over the winter was Peter Sagan who will be 32 for the entire season. Some of these riders will decline – some precipitously – while others will fend off age and produce just as strong as season as 2021.

In the aggregate though, these aging curves suggest teams which are more comprised of 30+ year old riders will fall-off more than those with younger riders. Among the World Tour teams, Israel Start-up Nation had the oldest roster in 2021 and now again in 2022 with their 30.8 year old average. Based on this aging curve, their riders are set to decline by 5% on average from their 2021 point totals. Since last year, their major additions were Nizzolo (age 33), Jakob Fuglsang (age 37), and Hugo Houle (age 31).

Team TotalEnergies races at the Pro Tour level and is the team which signed Peter Sagan (along with several of his mid to late 30s support riders). Their average team age ballooned from 28.9 in 2021 to 30.6 in 2022. They are also in-line for a 5% overall decline in their performance versus 2021. Those aren’t huge declines, but considering the salaries being paid to stars like Nizzolo, Fuglsang, and Sagan and the performances expected, they will be fighting against that current to produce.

Average Team age in 2021 and 2022

The younger teams most likely to improve collectively in 2022 mostly race at that Pro Tour level. Equipo Kern Pharma, Sport Vlaanderen, UNO-X, and Bardiani will all average under 25 years old in 2022. Those four are projected to improve just by aging by 7-9% in 2022 versus their 2021 performance.

However the most interesting team is Team DSM in the World Tour. DSM has added eight riders in 2022 – six of them under 25 – while they lost their two oldest riders from 2021. They are the only World Tour team with an average age under 27 in 2022 (25.7 years old). They are expected to improve collectively by around 5% versus 2021 performance by the aging curve. Riders like Kevin Vermaerke, Thymen Arensmen, Mark Donovan, and Andreas Leknessund all fit the bill of having previous World Tour experience + being aged 23 and under.