Disclaimer:

(Yes, this is another long post. That is because it is filled with facts. If facts without pictures is too much for you, then may I suggest a comic book instead. I have included a summary towards the end though.)

**HOW TO WIN CAT ENFORCEMENT EVENTS**

**INTRODUCTION**

They tested the cat-enforcement-with-added-benefits (i.e. with a new performance based cat model on top) on us. We criticized it. Their reply was to release it immediately and with no intention to change the model. Alright… so how does the model do?

Or we can put it differently, how do you win races using this new performance based model? And is there possibly something strange about how to win them? (Seeing as I and several others criticized it and predicted troubles…)

**METHOD**

I sampled as many cat C races using the new model as I could, using ZP, the test events included. Not that many races so far but many enough for some statistical testing. I discarded races with less than 10 ZP registered participants in cat C.

Although several of the latest races are crits on Downtown Dolphin there are also other races of varying length and with varying numbers of participants.

The reason for choosing cat C for this little study is it generally has high attendance, meaning more data, while at the same time being a rather low cat, meaning strange things going on with the new model will likely be more pronounced for various reasons than in cat B, another well-attended cat.

I then made several comparisons over a number of ZP measures between three sub-samples, using a statistical test called the Mann-Whitney U-test. This test can be used to check for differences between samples and it answers the question: if there is a difference in the averages of some measure between two samples, can it be just a random coincidence or is it rather highly unlikely that we wouldn’t see this difference again and again if we ran the test on new samples later on? (I.e. there IS a real difference.)

The Mann-Whitney test is a good choice when comparing samples when samples sizes differ and you don’t expect the measures you look at to be normally distributed, i.e. nicely bell shaped.

I have divided each race into three sub-samples. First I have made a comparison between podium winners (1st to 3rd) and the rest of the field. Then I have also compared the podium to 8th-10th place. The reason for the latter comparison is to get a fair number of racers who did put up a fight rather than use the race as a recovery ride, who thus still placed fairly high up in the results table (although there are some races with low attendance making 8th-10th a low placing), but who weren’t necessarily almost alongside the podium in the final sprint.

Below are the results and their implications for how to podium these events.

**RESULTS**

**20 min W/kg - Podium vs Losers (the rest of the field)**

Avg: 2.9 vs 2.8

Median: 2.9 vs 2.9

Sample sizes: 111 vs 788 racers

Significance: The result is statistically significant at the 1% level (p < 0.01). The p-value is 0.000435. It is therefore extremely unlikely that this difference, although small (2.9 vs 2.8), is random.

**20 min W/kg - Podium vs Early Losers (8th-10th)**

Avg: 2.9 vs 2.9

Median: 2.9 vs 2.9

Samples sizes: 111 vs 111

Significance: p = 0.77. p is NOT < 0.01. There is NO real difference between the podium and those going across the finish line fairly early with regards to 20 min W/kg.

The takeaway: *You don’t really win by having a higher 20 min W/kg than others in your cat. We have to look elsewhere.*

**Watt - Podium vs All Losers**

Avg: 240 W/kg vs 223 W/kg

Median: 242 W/kg vs 224 W/kg

Significance: p = 7.09E-07 (way smaller than 0.01). The difference is NOT random.

**Watt - Podium vs Early Losers**

Avg: 240W vs 229W

Median: 242W vs 233W

Significance: p = 0.017. The result is statistically significant at the 5% level but not at the 1% level. It is still quite unlikely that the difference is random.

Takeaway: *20 min W/kg doesn’t seem to matter much, not inside the category. But you do want to be able to push higher average Watts than others if you want to podium. Still, when Zwift remodeled the original new model, they suddenly chose to downplay the significance of raw Watt in favor of keeping W/kg measures as the primary cat divider. What were they smoking?*

**15 sec W/kg - Podium vs All Losers**

Avg: 7.7 W/kg vs 5.5 W/kg

Median: 7.5 W/kg vs 5.2 W/kg

Significance: p = 0. (A 64-bit computer can’t even calculate a non-zero value here.) The chance that this difference is random is infinitely small.

**15 sec W/kg - Podium vs Early Losers**

Avg: 7.7 W/kg vs 5.9 W/kg

Median: 7.5 W/kg vs 5.8 W/kg

Significance: p = 3.38E-14. The difference is NOT random.

Takeaway: *You want to have a strong sprint if you want to podium. No surprise there of course, but seeing as the new model is a mixed model that is supposed to take different parts of the power curve into account, don’t you think Zwift has undervalued the left end of the power curve? Just a little?*

**Height - Podium vs All Losers**

Avg: 180 cm vs 179 cm

Median: 180 cm vs 180 cm

Significance: p = 0.21. The very small difference (1 cm) is not statistically significant and could simply be random. I haven’t even bothered to compare the podium to early losers as I expect the result to be the same.

Takeaway: *Be average height. Or… height doesn’t really matter for race results (although we know it affects speed slightly in Zwift’s physics model).*

**Weight - Podium vs All Losers**

Avg: 84 kg vs 81 kg

Median: 85 kg vs 81 kg

Significance: p = 0.0035. The difference is not random.

**Weight - Podium vs Early Losers**

Avg: 84 kg vs 81 kg

Median: 85 kg vs 80 kg

Significance: p = 0.016, significant at the 5% level.

Takeaway: *Be heavy! I tested for weight in the old cat limits ages ago and the result was the same back then as it is today with the new and “improved” model. Heavy set racers with a larger muscle volume have an advantage. Also, juniors with very low weight have an advantage too but for different reasons (juniors were included in the podium sub-sample), and I expect the average weight difference between podium and losers to be even bigger if the juniors on the podium are discarded (gonna test this later). The heavy guy can usually push higher Watts while keeping in line with W/kg limits and the lighter guy usually has a hard time keeping up. And if he still does keep up, then he might not get the chance to race against the heavies anyway since the model might move him up to cat B. Why haven’t they accounted for weight in the model? Oh, it’s because they’re still stuck on W/kg. Fair? Fun? Zwift… you suck! You* still *suck!*

**Avg-to-Max HR - Podium vs All Losers**

Avg: 0.85 vs 0.89

Median: 0.86 vs 0.90

Significance: p = 1.33E-15. No way in ■■■■ the difference is random.

**Avg-to-Max HR - Podium vs Early Losers**

Avg: 0.85 vs 0.89

Median: 0.86 vs 0.91

Significance: p = 7.22E-10. NOT random, no chance.

Note: This is an interesting ratio, comparing the avg HR to the HR peak during the race. Someone with a very low difference between the two measures is either holding back his efforts evenly across the race (not that common) or is close to a flat out effort with little variation for most of the race (more likely). By contrast, someone with a large difference between the two measures is doing a relatively low effort during most of the race compared to his max, which in turn will be his effort level at select moments, e.g. in the final sprint or in some crucial climb short enough to go hard in.

Takeaway: *Keep cruising! It is still, with the new model in place, possible to stay in cat indefinitely and do a lesser effort, intentionally or not, than others putting up a fight, and still grab podiums. The cruising just isn’t as blatantly obvious as before since there is no transparency around the cat definitions anymore. You have to do a little statistics first to unearth the stink. And now I have. There it is, the proof that the new model doesn’t prevent cruising. Of course it doesn’t. It can’t, not as long as W/kg remains an important element in the model. This is what you get, epic fail. Shame on you, Zwift!*

**EXECUTIVE SUMMARY FOR COMICS FANS**

This is how to improve your chances of winning the cat enforced races:

Be a relatively heavy sprinter, toward the upper end of the W/kg spectrum in your cat (like most others), a guy [sic!] who can also push heavy Watts on the flat. Oh, and don’t push yourself too hard when there is no need to (unlike in a sprint or a short climb). That will only get you promoted to the bottom of the next cat and that will be no fun.

In other words, little has changed with the new model. Surprise, surprise…