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.

In-transfers

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.

Out-transfers

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).

Limitations

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.

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