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.

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