There are broadly four domains in professional cycling: sprints, climbs, time trials, and all-around (the sort of ability which wins one day races, breakaways, and short uphill finishes). Measuring performance – with an eye to using in a predictive sense – is necessarily different in each area.
- For sprints, there’s no time gaps so finish position is what matters.
- For time trials, the clock is all that matters.
- For climbing, generating time gaps is critical, but there needs to be an opportunity to do so.
- For one day races or breakaways, it can be just as much about how often you make the break or key move as how often you finish it off with a win or podium place.
To measure each, I’ve developed metrics to answer the key question posed by each discipline:
SPRINTS: how high are you finishing?
CLIMBS: how much separation can you generate on your opponents?
TIME TRIALS: how fast can you go against the clock?
The necessary first step before measuring performance in each discipline is actually classifying races between disciplines (see this earlier post).
We’ll cover Sprints, Uphill Finishes, and Time Trials today, with analysis of performance in mountain stages to come later this week.
For sprints, I’ve developed the metric Log Points. Riders get points for finishing in 10th place or better on a sprint stage based on a formula which heavily weights finishing in the highest positions.
|Finish Position||Points Earned|
This means riders who consistently finish in the top three will be ranked around 0.5. Riders who finish outside the top 5 will be ranked around 0.05. This represents that the gulf between the top sprinters and the rest is wide – the top sprinter in each Grand Tour since 2013 have won 40% of sprint stages they’ve contested.
The ranking since the start of 2018 shows Groenewegen out in front followed by Sam Bennett, and a trio of Gaviria, Viviani, and Ackerman.
|Rider||Average Log Points||Stages|
|Dylan Groenewegen tdf||0.49||26|
|Eli Viviani tdf||0.40||55|
|Peter Sagan tdf||0.37||42|
|Michael Matthews tdf||0.35||18|
|Caleb Ewan tdf||0.27||46|
|Jasper Philipsen tdf||0.26||16|
Yearly leaders since 2015 are:
- 2015: Peter Sagan (0.45)
- 2016: Peter Sagan (0.52)
- 2017: Peter Sagan (0.50)
- 2018: Dylan Groenewegen (0.52)
- 2019: Sam Bennett (0.55)
By this method, Peter Sagan’s last 18 months have been a level below his 2015-17 performance (0.37 vs 0.49).
Looking at odds for TDF Stage 1 field sprint, Pinnacle has Groenwegen ~35%, Viviani ~25%, Ewan ~20%, and Sagan ~15% to win.
Sharp uphill finishes at the finish line are unique breed of flat stage. Like stages designated as sprint stages, they tend to be long, fast, and mostly flat.
Sprint stages have an average climb difficulty of just 3.5 vs 4.4 for uphill finish stages (where 40+ is high mountains). The difference is sprint finishes have an average gradient of 0% in the final kilometer vs 5% for uphill finishes. These can range from selective, sharp closing climbs like Mur de Bretagne in last year’s Tour de France to the more mild rises like the immediate prior stage in Quimper.
Log Points is again the measure on these stages. Time gaps taken tend to be small – a median gain by the stage winner of 3 seconds on 10th and 9 seconds on 20th (versus 0 seconds on sprint stages) – so finish position is again the crucial metric.
Ranking the peloton since the start of the 2017 Tour de France sees Valverde, Alaphilippe, and Caleb Ewan as the far and away top three. Sagan comes in fifth and we see some other familiar faces like Daryl Impey and Greg van Avermaet.
|Rider||Average Log Points||Stages|
Important to remember sample size on these type of races is much smaller than for sprint stages.
One missing man is excluded for that reason; Michael Matthews has just two of these type of races in the last two years, but dating back to 2013 he’s behind only Alaphilippe, Sagan, and Valverde on these type of stages.
To measure time trial performance, the obvious elements to consider are your time and the distance of the race. It’s typical for short prologues to be won by a handful of seconds (13 seconds separated 1st and 50th in the 4km 2019 Romandie prologue vs 98 seconds between 1st and 50th in the 17km Stage 5 ITT), while longer TTs create more separation.
Another crucial factor is that the time trial is relevant for only a handful of riders – GC competitors and TT specialists – meaning we care much less about how well the 50th placed rider performed than the 10th placed rider. In Grand Tours since 2013, the median rank for the eventual GC winner in time trial stages is 5.5 with a median gain of 0.4 seconds/KM on the 10th place finisher in the stage.
Our metric will be Relative Speed and will simply be the seconds gained on 10th place in the stage divided by the length of the course in kilometers.
For example, Fabio Aru entered the 2015 Giro Stage 14 ITT leading Contador by 19 seconds. The 59km course is the longest in recent Grand Tours meaning Contador had a lot of opportunity to pick up time on Aru. Contador gained 82 seconds on 10th place, while Aru lost 85 seconds – a difference of nearly three minutes which would give him his winning margin in the race. Contador gained 1.4 seconds/KM, while Aru lost -1.4 seconds/KM.
The median Relative Speed for time trial winners in Grand Tours since 2013 is about 3.0 seconds/KM. In this year’s Tour de France Stage 13 ITT is 27km which translates into a projected gain of about 81 seconds for the winner over 10th place on the stage.
However, going by this method is not perfect as uphill TTs like Stage 1 in this year’s Giro provide much a much larger opportunity to gain time than a flat TT. Eg, Roglic took 5 seconds/KM on 10th place over 8km (not all uphill) in that stage, while Wout van Aert took 2.7 seconds/KM on 10th place over 26km in the much flatter Dauphine TT this year.
We add an adjustment to relative_speed based on the total vertical meters gained divided by the length of the stage (eg, in that Stage 1 Giro TT the route moved uphill 203m over 8km so 0.026). The equation is:
adjustment_factor = 0.6 + (31.1 * (total_meters_gained / length_in_m)) adj_relative_speed = relative_speed / adjustment_factor
For that Giro example, the adjustment_factor was 1.4 which yields an adj_relative_speed of 3.6 for Roglic – still very strong when compared with other TTs.
Since the start of the 2017 Tour de France, Rohan Dennis and Tom Dumoulin are the stand-outs in the TT. Against the strongest opposition in grand tours and the world championships, Dumoulin has finished 5th, 2nd, 1st, 3rd, 1st, and 1st in the last 24 months.
|Rider||Median Relative Speed||Stages|
|Rohan Dennis||2.3 seconds / KM||13|
|Tom Dumoulin||1.9 seconds / KM||10|
|Victor Campanaerts||1.5 seconds / KM||15|
|Stefun Kung||1.3 seconds / KM||15|
|Soren Kragh Andersen||1.1 seconds / KM||9|
|Michal Kwiatkowski||1.0 seconds / KM||13|
|Tony Martin||0.9 seconds / KM||14|
|Primoz Roglic||0.8 seconds / KM||14|
|Patrick Bevin||0.7 seconds / KM||12|
|Chris Froome||0.6 seconds / KM||9|
None of the GC contenders on this list are in the 2019 TDF field, but Bernal (0.5 seconds / KM) and Thomas (0.2 seconds / KM) have both performed well in this discipline.
Equally as important when talking about time trials and the TDF is how bad certain riders are in this discipline. It’s no secret Romain Bardet struggles; he’s given away about -4.3 seconds/KM to 10th place in ITTs in the last 24 months. Comparing him to Geraint Thomas – for example – sees him losing about 120 seconds over the 27km Stage 13 ITT this year.
Most other GC contenders are between -1.5 and -3.5 seconds / KM (Porte, Pinot, Yates, Martin, and Nibali are all in this zone). Jakob Fuglsang (-0.5 seconds / KM) is probably the best placed GC contender to tangle with Thomas/Bernal in this discipline; he should be able to keep his losses under 30 seconds.
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