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Month: March 2021

Shooting Efficiency: 2019โ€“20 vs. 2020โ€“21

Posted on 2021-03-30 | by real biathlon | Leave a Comment on Shooting Efficiency: 2019โ€“20 vs. 2020โ€“21

After examining changes in skiing speed, let’s also look at a comparison of overall shooting quality between the 2019โ€“20 and the 2020โ€“21 seasons for all regular Biathlon World Cup athletes. To do that, I came up with the concept of Shooting Efficiency, an attempt to combine shooting accuracy and shooting time into one metric. For more details how it’s calculated, see here.

If you can’t find a specific athlete, you can look up the complete World Cup field (also available per trimester) for latest season (as well as all previous seasons) here:

  • 2020โ€“21 Shooting Efficiency: Men | Women

Note: Only athletes with at least 4 non-team races last season and 16 non-team races this winter are included in the tables below. Shooting Efficiency is an overall shooting score, combining shooting accuracy and shooting time. It is the theoretical average time an athlete loses through shooting (based on hit rate, range time and potential penalty loops). For more details, see here.


Men

Lukas Hofer improved his non-team hit rate by 7.1% and managed his quickest shooting times (avg. 29.1s) since the 2009โ€“10 season โ€“ which makes him the most improved among regular starters in the men’s field. The overall most efficient shooter, Simon Eder, also improved significantly over last season: he set his career best hit rate (93.3%) and his average theoretical time loss of 1:48.9 is the fastest ever for this Shooting Efficiency score.

If you have been wondering why Johannes Thingnes Bรธ had to fight so hard to defend his title (despite being close to his best ever ski speed), this stat gives the answer: in a sprint he loses the time equivalent of almost an entire additional penalty loop (roughly 2 penalty loops in pursuits/mass starts) compared to last winter (-6.9% hit rate). Sturla Holm Lรฆgreid was the overall 2nd best shooter, thanks to outstanding hit rate (92.6%) and great range times (46.8s). Interestingly, Lรฆgreid’s range time is faster than Eder’s, even though Eder’s shooting time is 0.6s better; apparently Lรฆgreid’s shooting preparation is close to one second quicker.

Changes in Shooting Efficiency | 2019โ€“20 vs. 2020โ€“21

NoFamily NameGiven NameNationRacesHit RateRange TimePenalty LoopTime Loss
Sprint
Change
NoFamily NameGiven NameNationRacesHit RateRange TimePenalty LoopTime Loss
Sprint
Change
1HoferLukasITA
2684.2948.820.22:09.3-20.2
2SimaMichalSVK
1684.0950.422.92:17.1-17.9
3PonsiluomaMartinSWE
2679.5247.521.62:19.3-16.1
4SamuelssonSebastianSWE
2687.1449.521.22:06.2-14.3
5EderSimonAUT
2693.3347.022.31:48.9-12.1
6GowChristianCAN
2187.5048.422.02:04.3-12.0
7WegerBenjaminSUI
2486.8450.222.32:09.7-10.9
8LesserErikGER
2386.9448.122.02:04.8-9.9
9NordgrenLeifUSA
1785.4250.323.12:14.2-9.5
10GowScottCAN
1982.8646.823.02:12.9-9.4
11EliseevMatveyRUS
2589.0046.722.11:57.7-9.4
12LatypovEduardRUS
2582.7550.621.82:18.9-9.3
13RastorgujevsAndrejsLAT
1980.3350.521.42:23.2-5.9
14DovzanMihaSLO
2088.0046.022.91:59.5-5.6
15VarabeiMaksimBLR
2078.3354.822.42:38.3-4.9
16HarjulaTuomasFIN
1786.9650.422.22:09.7-3.9
17KrcmarMichalCZE
2086.5650.321.72:09.7-3.9
18FemlingPeppeSWE
2182.8147.822.82:14.9-3.5
19LeitnerFelixAUT
2385.0053.122.22:19.5-2.6
20LangerThierryBEL
1881.1553.922.82:30.6-2.0
21FakJakovSLO
2690.7147.921.71:55.9-1.8
22SeppalaTeroFIN
2179.6951.121.62:26.2-1.0
23IlievVladimirBUL
1775.6051.022.02:35.5-0.8
24WindischDominikITA
1976.5550.121.12:29.6-0.6
25ClaudeFabienFRA
2579.0048.421.32:21.5-0.0
26BocharnikovSergeyBLR
2282.9450.824.32:23.0+0.1
27KomatzDavidAUT
2290.5953.921.72:08.2+0.7
28ClaudeFlorentBEL
1985.3654.022.22:20.5+0.8
29DohertySeanUSA
1982.1449.322.22:18.3+1.1
30DollBenediktGER
2681.4348.321.92:17.1+1.4
31Fillon MailletQuentinFRA
2587.2546.622.92:02.5+1.7
32JacquelinEmilienFRA
2687.3846.921.12:00.4+2.2
33BoeTarjeiNOR
2685.7149.920.32:08.8+2.3
34StroliaVytautasLTU
1678.1853.122.72:35.6+2.8
35PeifferArndGER
2187.9448.721.42:03.1+3.1
36BormoliniThomasITA
1884.0749.722.12:14.5+3.2
37PrymaArtemUKR
1982.8648.223.12:16.0+3.5
38DesthieuxSimonFRA
2685.2447.821.42:07.1+3.6
39DombrovskiKarolLTU
1786.2553.423.12:18.5+3.8
40ChristiansenVetle SjaastadNOR
2286.0050.421.12:10.4+4.4
41DaleJohannesNOR
2683.8152.021.62:18.9+4.7
42GaranichevEvgeniyRUS
1886.5549.823.42:11.2+4.8
43SmolskiAntonBLR
2280.5950.822.22:24.6+5.1
44LaegreidSturla HolmNOR
2692.6246.821.21:49.2+5.3
45GuigonnatAntoninFRA
2581.7549.121.92:18.2+6.6
46MoravecOndrejCZE
1886.0748.722.62:08.8+9.5
47PidruchnyiDmytroUKR
2280.5947.022.82:18.2+10.6
48TrsanRokSLO
1885.1947.823.32:10.2+11.5
49LoginovAlexanderRUS
2486.3249.022.42:08.6+12.0
50NelinJesperSWE
2374.0552.021.72:40.4+13.0
51BrownJakeUSA
1776.0055.030.53:03.1+14.0
52FinelloJeremySUI
1970.3650.422.22:46.6+14.2
53StvrteckyJakubCZE
1971.4356.221.72:54.5+15.1
54KuehnJohannesGER
1675.0053.921.22:40.7+15.2
55BoeJohannes T.NOR
2685.2448.821.12:08.7+18.2


Women

Janina Hettich was the most improved shooter on the women’s side. In her 9 races in 2019โ€“20, she had only managed to hit 70.9% of her targets โ€“ she was 17.7% better this winter. Dzinara Alimbekava wasn’t just the most improved skier, she was also the 2nd-best in terms of shooting improvements (further highlighting her incredible breakout year). Karoline Offigstad Knotten and Dorothea Wierer were the overall most efficient female shooters; they did however lose roughly 20s more on the range compared to Eder/Lรฆgreid (maybe 4-5s of that is down to skiing, the rest is due to slower and less accurate shooting).

Overall World Cup winner, Tiril Eckhoff, improved her shooting somewhat, thanks to a slightly higher hit rate (+1.4%) and a lower shooting time (-1.8s). In general, Eckhoffโ€™s performance stats, in terms of neither skiing nor shooting, improved dramatically; however, her Overall Performance Score nudged 0.1 higher (even with two horrendous races at the season opener). Hanna ร–berg‘s shooting closely followed her skiing form: she was the top shooter in trimester 1 (90.0% hit rate), but it completely fell apart by the end of the season (trimester 3 hit rate: 70.9%).

Changes in Shooting Efficiency | 2019โ€“20 vs. 2020โ€“21

NoFamily NameGiven NameNationRacesHit RateRange TimePenalty LoopTime Loss
Sprint
Change
NoFamily NameGiven NameNationRacesHit RateRange TimePenalty LoopTime Loss
Sprint
Change
1HettichJaninaGER
2488.6154.525.22:17.8-38.9
2AlimbekavaDzinaraBLR
2683.8153.025.12:26.7-28.1
3HaeckiLenaSUI
2179.6947.425.42:26.5-20.1
4MinkkinenSuviFIN
1883.8551.225.92:24.2-15.4
5ZdoucDunjaAUT
2588.7553.524.22:14.2-15.2
6ZukKamilaPOL
1777.2057.225.02:51.5-14.2
7MironovaSvetlanaRUS
2277.9451.223.92:35.1-13.7
8OebergElviraSWE
2582.2550.924.72:25.7-13.5
9HammerschmidtMarenGER
1786.9249.525.92:12.9-13.4
10KnottenKaroline O.NOR
2389.1750.325.72:08.4-13.3
11DavidovaMarketaCZE
2582.7554.023.82:29.1-11.8
12DunkleeSusanUSA
1979.6453.426.32:40.2-9.7
13HinzVanessaGER
2287.0653.324.92:18.8-8.0
14TachizakiFuyukoJPN
1882.3156.725.62:38.8-7.9
15KruchinkinaElenaBLR
2377.2258.825.22:55.0-7.1
16EderMariFIN
1777.0858.824.82:54.3-6.7
17EckhoffTirilNOR
2684.5251.023.02:17.6-6.5
18HerrmannDeniseGER
2581.5052.624.12:29.7-6.3
19SchwaigerJuliaAUT
1984.6454.625.22:27.9-6.1
20LunderEmmaCAN
2285.8849.824.92:14.7-5.0
21Braisaz-BouchetJustineFRA
2676.6753.623.32:41.7-4.1
22CadurischIreneSUI
1679.5748.324.82:27.3-3.9
23GasparinSelinaSUI
1977.5054.623.62:42.2-3.8
24WiererDorotheaITA
2686.9049.023.92:09.3-3.7
25PidhrushnaOlenaUKR
1882.9653.126.02:30.3-3.0
26BrorssonMonaSWE
2284.4153.724.92:26.4-2.0
27DzhimaYuliiaUKR
2186.5652.024.82:17.4-1.9
28Hojnisz-StaregaMonikaPOL
1687.2052.924.62:17.3-1.7
29KuklinaLarisaRUS
1684.0049.424.92:18.7-0.7
30GasparinElisaSUI
2180.9452.024.92:31.4+0.2
31SolaHannaBLR
2370.2749.924.22:51.7+0.2
32PuskarcikovaEvaCZE
1882.6950.426.22:26.1+0.8
33TalihaermJohannaEST
1880.7756.926.12:43.9+1.3
34PerssonLinnSWE
2683.8153.123.92:24.9+2.1
35RoeiselandMarte OlsbuNOR
2685.0050.524.22:17.3+2.1
36TandrevoldIngrid L.NOR
2583.0854.824.12:30.4+2.5
37HauserLisa TheresaAUT
2685.0050.923.02:16.4+4.9
38BescondAnaisFRA
2683.1056.624.22:34.0+5.1
39MaedaSariJPN
1671.8256.625.73:05.8+6.3
40OebergHannaSWE
2684.5248.224.22:13.8+6.7
41GasparinAitaSUI
1983.5751.025.92:24.6+7.3
42VittozziLisaITA
2578.0051.524.42:36.8+8.4
43JislovaJessicaCZE
1777.0856.325.32:50.5+8.8
44EganClareUSA
2381.6757.724.32:40.0+8.9
45PreussFranziskaGER
2686.4350.523.42:12.7+9.7
46SimonJuliaFRA
2675.5048.024.52:36.0+11.0
47BendikaBaibaLAT
1676.6750.524.82:39.0+11.1
48TodorovaMilenaBUL
1976.9056.424.82:50.0+12.2
49CharvatovaLucieCZE
1765.9151.623.13:02.1+12.4
50LieLotteBEL
1890.0055.226.22:16.7+13.5
51BlashkoDaryaUKR
2087.7452.725.02:16.1+14.3
52ChevalierChloeFRA
1878.1557.724.32:48.4+16.3
53InnerhoferKatharinaAUT
1766.6754.424.13:09.2+22.9

Posted in Statistical analysis | Tagged shooting

Wierer in pursuit… of my mind

Posted on 2021-03-28 | by biathlonanalytics | Leave a Comment on Wierer in pursuit… of my mind

In early March I wrote a piece about Wierer’s Pursuit efforts and results, and although I still stand by what I wrote it somehow felt incomplete. In the latest Extra Runde podcast, they brought up again that Wierer seems to do better when starting with a lower bib number in the Pursuit. It feels right, but I couldn’t find the data to support that for Wierer specifically. I did find that the later you start, the more places you can, and typically will, make up, but that applied to all athletes.

Then it struck me (yes, I’m a bit slow sometimes…) there was another way to measure performance in the Pursuit that could be helpful. Look at the Isolated Pursuit time, or in other words the “race time” – “seconds behind at start”. You can also call it the actual race time, and it’s a simple calculation: Total Time – Start Info. The latter has the time an athlete started behind Bib nr. one, the winner of the prior Spring race. Having this Isolated Pursuit time and the ranking of this time would show me her true performance of the day. Plotting that for the last three seasons gives me the following chart:

Now we can see if her start bib is higher her Isolated Race results vary between very good and very bad. Her races with a bib number higher than 15 though are all good to very good for the Isolated Race performance.

Values show change in ranking of isolated race result and start bib (positive = improvement)

If we only take her 8 Pursuit races in the 2020-2021 season we can see she had her best performances when starting lower than 5th and two of her three worst performances when starting 5th or higher. I think it’s fair to say, although based on a small sample size, that the guys from Extra Runde were correct in their assumption. Starting later in the Pursuit races brings up the better performances in Dorothea Wierer. Now they have some data to prove it.

Data from RealBiathlon.com, Feature image from Manzoni/IBU

Posted in Statistical analysis

Stina, look at what you made me do!

Posted on 2021-03-28 | by biathlonanalytics | Leave a Comment on Stina, look at what you made me do!

After watching the final season races in Oestersund, and one moment in one race specifically, I wanted to do further analysis into the time that athletes prepare themselves for shooting and for skiing after the shooting. And see if this would be even possible. But first, let me show you the moment that triggered this:

Stina Nilsson takes FOREVER to put her poles back on and get back to fully functioning skiing after her prone shoot in the Women’s Sprint race in Oestersund. It made me wonder if there is a way to analyze how fast or slow athletes are outside of the shooting while not skiing on the course. Taking poles and rifle off, getting in position, getting the rifle and poles back on and getting skiing again.

Data

The data I hope we can use for this is the Range time and the Shooting Time. As I’m interested in the time on the Range but while not shooting, it’s a simple subtraction: Range time – Shooting time = Prep time. So Prep time is the time spent in the Range while not shooting. That would be the time described above, getting off the poles and rifle, getting ready, and then getting moving again.

The only problem is that there are athletes like Stina Nilsson that take so long to get the poles back on that they already have left the range. Now I’m sure this happens more often (especially for those athletes shooting in the lane closest to the penalty zone at end of the range) but I don’t recall ever seeing an athlete going past the time recording and still having to start putting on the second pole!

Can we use this data then?

Ironically when I look at Stina’s race data, her Range time rank is 57th (103.1 sec.) and her Shooting time rank is 70th (62.3 sec.). But she does have the fastest Prep time of the whole race. We know that a) her prep goes well beyond the range, and b) there is logic in that the more time you spend shooting while in the range, the less time you have for Prep while in the Range. Considering this, the Range and Shooting time are not able to answer who is faster and slower at “prepping”, and I don’t think there is data available that can actually answer this question.

Alternative insights perhaps?

What is still interesting though is to look at how much of the Range time is used for shooting. At least that will tell something about the time spent not shooting while in the Range. In this specific race, it varied from 33% to 53% of Range time, which tells you there is still a lot of time to be gained by either skiing / gliding a bit faster in the range before the shooting, getting in position faster, and getting up and going after the shooting.

Phases

Another fact we cannot get from the data is that the phases before and after the shooting are very different. The phase before shooting is focused on slowing down, reducing the heart rate and focussing on the shooting. In other words, this tends to be slow. The phase after the shooting is fast and all focussed on getting ready and on to the course to ski.

Depending on what lane the athlete shoots in, the distance spent in these two phases while inside the Range area differs significantly. This also means we cannot look at these data for one race and draw any conclusions. For all races in three seasons, however, this should even out to some degree, although I would expect that the top athletes who shoot in the last lanes most of the time spend more time in the slow phase while in the Range area.

Alternative insights perhaps? – Part II

Sticking with the women’s field but including all races since the 2018-2019 season, we now see the average percentage of Range time spent shooting varies from 45% to 70%. Or 30% to 55% spent not shooting. Let’s check that assumption that higher-ranked athletes would be slower because they relatively spend more time in the slow phase due to their shooting lane. first I look at the average rank and average time spent shooting per athlete:

This only shows us that as the Ranks get higher there is more variability between the athletes. How about the same but per athlete and race:

The trend line is barely statistically significant (p ~ 0.05%) but the R-square value tells me not much variation is explained by the model.

What ever it does and does not tell me, my question if higher ranked athletes are spending more time in the slow phase and thus slower in the time spent not shooting is not answered. On top of that, higher ranked athletes are generally speaking better shooters, both accuracy and speed, so that is another variable in the mix.

Conclusion

My sad conclusion is that based on the available data we cannot make any conclusions about how fast or slow athletes are prepping for shooting and then for skiing. If we would have data that starts from the moment the athletes prepares for shooting and continues until an athlete is in full skiing position, I don’t see how I can come to any conclusion on this topic. Perhaps I should rename this piece “Ramblings about Preparation time.” Nah. Hopefully, these ramblings will trigger something with readers and who knows where that may lead?

Posted in Statistical analysis

Women’s field narrows but men’s stays the same

Posted on 2021-03-26 | by biathlonanalytics | Leave a Comment on Women’s field narrows but men’s stays the same

As a follow up on my previous post, Number of winning athletes doubles in super exciting season, this analysis looks at the narrowing or broadening gap between performances in non-team biathlon races since the 2009-2010 season. Performance, in this case, is measured as the average number of seconds behind the race winner for the 10th, 30th and 60th ranked athletes. So on a seasonal average, how close to the leader are the top 10, top 30 and top 60 in races during the season and how does that compare to previous seasons?

For all charts below, men are on the left, women on the right.

Sprint

For the sprint events, we can see that the men’s field hasn’t changed much over the years and actually has gotten more stable for the top 30 and top 10. For the women, however, we can see the field has narrowed, especially with the “lower” athletes closing in on the top: the top 60 have gone from about 200 seconds back to about 140 seconds back. That’s a minute in just over 10 years! The top 30 and even the top 10 have also come a little closer, meaning the field is compressing and the differences between the fastest and the slowest are getting smaller.

Pursuit

For the pursuit the top 60 was removed as with 60 starters only and always a few DNSs or DNFs there typically is no 60th ranked athlete.

The picture is simular to the Sprint, where the men haven’t realy change much over the years, but the women’s field has significantly narrowed from 0ver 250 seconds behind the leader to about 180 seconds behind, more than a minute for the top 30 to get closer to the lead.

It should be noted that the time behind the lead here includes the time behind the race leader at the start (as per usual in pursuit races), so we’re not using absolute race times here.

Individual & Mass Start

The individual races have seen more fluctuations in the last couple of years for the men, but the gap has remained similar. For the women, again, we see the field closing in by almost a minute-and-a-half by the top60half a minute by the top 30 and about 10 seconds by the top 10. That is quite significant.

Lastly for the mass starts, with only 30 starters and occasionally some athletes not making the finish, we see a small widening of the gap for the men and more or less consistency for the women.

Conclusions

The performance gap for the men has stayed pretty much the same for all events, if anything widening a little for the mass starts. For the women however the gap has declined considerably for all events but mass starts.

An interactive version of the charts can be found here.

Posted in Biathlon News, Statistical analysis | Tagged Seconds behind

Ski speed comparison – 2019โ€“20 vs. 2020โ€“21

Posted on 2021-03-23 | by real biathlon | Leave a Comment on Ski speed comparison – 2019โ€“20 vs. 2020โ€“21

Skiing was once again by far the most important factor to determine an athlete’s eventual World Cup rank. Not only did the two outright fastest skiers, Johannes Thingnes Bรธ and Tiril Eckhoff win the overall title, but out of the ten fastest skiers (per gender), eight made the World Cup top 10 on the men’s side, six on the women’s side. Let’s look at who managed to improve and whose ski speed declined season-to-season.

You can check out full season statistics for all World Cup athletes here:

  • Ski speed: Men | Women
  • Shooting percentage: Men | Women
  • Shooting Times: Men | Women

For patrons, the comparisons page allows you to compare all shooting and skiing stats on your own, not only season-to-season, but also by trimester.


Note: Only athletes with at least 4 non-team races last season and 15 non-team races this winter are included in the tables. “Back from Top30 median” is the percentage back from each race’s top 30 median Course Time (arithmetic mean per season).


Men

Tuomas Harjula and Jeremy Finello were the most improved skiers this season, both roughly 3.5% faster compared to 2019โ€“20. Erik Lesser had an injury-affected winter last year and bounced back to his previous level. Johannes Kรผhn, the fourth-fastest skier last season, never managed recover from a pre-season injury and ended the season with the biggest decline among regular starters.

Johannes Thingnes Bรธ was not as dominating as many expected after the retirement of Martin Fourcade, however, his ski speed was not to blame: he set the top ski time in 20 out of 26 races. Sturla Holm Lรฆgreid improved a lot, albeit he only appeared in four races last winter. Percentage-wise Quentin Fillon Maillet had a big decline in his skiing performances, even though his course times ranks were only 1.9 positions lower and he he finished the season third overall. ร‰milien Jacquelin‘s average was heavily influenced by the WCHs mass start (+12.1%); without that one race his speed would have been roughly 0.5% better.

NoFamily NameGiven NameNationRacesSki Rank
(avg)
Changeback from
Top30 median
(in %)
Change
NoFamily NameGiven NameNationRacesSki Rank
(avg)
Changeback from
Top30 median
(in %)
Change
1HarjulaTuomasFIN
1760.3-27.7+5.05-3.64
2FinelloJeremySUI
1924.3-34.4+0.84-3.47
3KomatzDavidAUT
2246.4-36.6+3.40-3.32
4LesserErikGER
2320.6-28.6+0.50-3.07
5PonsiluomaMartinSWE
268.8-18.3-1.16-2.68
6DovzanMihaSLO
2060.4-20.3+5.26-2.47
7SamuelssonSebastianSWE
2612.0-16.7-0.71-2.17
8LaegreidSturla HolmNOR
2611.2-12.3-0.79-2.16
9LatypovEduardRUS
2517.0-19.0+0.14-2.10
10SmolskiAntonBLR
2225.7-17.0+1.03-1.71
11BocharnikovSergeyBLR
2235.0-14.2+2.09-1.59
12BrownJakeUSA
1731.3-16.0+1.72-1.46
13SimaMichalSVK
1671.1-14.4+6.06-1.31
14GowChristianCAN
2150.1-11.6+3.50-1.24
15HoferLukasITA
269.4-6.3-1.04-1.15
16LangerThierryBEL
1854.6-5.5+4.08-1.03
17DombrovskiKarolLTU
1762.6-7.7+4.92-1.00
18FakJakovSLO
2622.7-7.3+0.70-0.99
19DaleJohannesNOR
265.5-4.4-2.00-0.82
20NelinJesperSWE
2316.2-8.4+0.16-0.79
21StvrteckyJakubCZE
1931.8-6.0+1.61-0.77
22VarabeiMaksimBLR
2041.4-10.3+3.12-0.74
23DudchenkoAntonUKR
1558.5-2.9+4.37-0.64
24GuigonnatAntoninFRA
2522.2+0.2+0.60-0.54
25MukhinAlexandrKAZ
1571.5-3.0+5.85-0.50
26ChristiansenVetle SjaastadNOR
2214.7-2.5-0.37-0.37
27WegerBenjaminSUI
2426.7-2.0+1.21-0.36
28PeifferArndGER
2113.9-1.7-0.39-0.34
29NordgrenLeifUSA
1759.4+1.9+4.40-0.30
30DohertySeanUSA
1947.6-1.9+3.32-0.26
31EderSimonAUT
2633.9-1.6+2.00-0.25
32BormoliniThomasITA
1843.2-3.6+2.96-0.23
33KrcmarMichalCZE
2027.3+0.1+1.28-0.20
34BoeTarjeiNOR
266.4-1.6-1.52-0.20
35BoeJohannes ThingnesNOR
261.7-1.3-3.10-0.20
36SeppalaTeroFIN
2128.0+0.1+1.17-0.17
37GuzikGrzegorzPOL
1562.5-2.1+4.95-0.06
38RastorgujevsAndrejsLAT
1919.8+2.2+0.42-0.04
39LoginovAlexanderRUS
2417.6+0.2+0.10+0.03
40ClaudeFabienFRA
2517.0+2.0+0.08+0.03
41WindischDominikITA
1924.2+1.6+0.82+0.08
42EliseevMatveyRUS
2533.0+5.4+2.17+0.08
43GaranichevEvgeniyRUS
1836.1-0.2+2.36+0.15
44TrsanRokSLO
1870.1-2.7+6.71+0.26
45JacquelinEmilienFRA
2611.8+0.1-0.22+0.27
46DollBenediktGER
2612.2+2.3-0.72+0.28
47PidruchnyiDmytroUKR
2229.8+5.8+1.57+0.32
48DesthieuxSimonFRA
2615.4+3.1-0.18+0.46
49ClaudeFlorentBEL
1945.4+5.2+3.44+0.49
50MoravecOndrejCZE
1844.6+4.9+3.32+0.50
51PrymaArtemUKR
1937.4+9.0+2.23+0.58
52LeitnerFelixAUT
2328.7+5.1+1.50+0.61
53GowScottCAN
1956.4+5.5+4.30+0.65
54StroliaVytautasLTU
1649.9+7.2+3.62+0.65
55FemlingPeppeSWE
2150.5+2.9+3.94+0.70
56Fillon MailletQuentinFRA
257.6+1.9-1.35+0.72
57IlievVladimirBUL
1731.0+9.0+1.61+0.92
58KuehnJohannesGER
1619.5+12.8+0.34+1.69


Women

Dzinara Alimbekava was the most improved skier in the women’s field, skiing an incredible 5.8% faster than last year and lowering her average ski rank by 49.6! Dunja Zdouc managed to improve even more than her Austrian teammate Lisa Theresa Hauser. Skiing 3.9% faster, Hanna Sola set the fastest ski time twice this winter and made her first two career podiums. After setting the fastest ski time in one of the races at the season-opener in Kontiolahti (for the first time in her career), Hanna ร–berg had an abysmal final month of the season.

Tiril Eckhoff dominated the World Cup action after Christmas โ€“ not because of improved skiing however, she remained more or less at the same (high) level as last year. Dorothea Wierer‘s skiing improved slightly over the course of the season, but last year’s World Cup winner was still 1.2% slower, which gave her little chance to defend her title. Denise Herrmann, last winter’s overall fastest saw her ski speed decline considerably, even though she still remained one of the field’s top 5 skiers.

NoFamily NameGiven NameNationRacesSki Rank
(avg)
Changeback from
Top30 median
(in %)
Change
NoFamily NameGiven NameNationRacesSki Rank
(avg)
Changeback from
Top30 median
(in %)
Change
1AlimbekavaDzinaraBLR
2613.8-49.6-0.30-5.79
2ZdoucDunjaAUT
2541.4-35.2+2.92-4.39
3SolaHannaBLR
2311.5-35.9-0.56-3.85
4BeaudrySarahCAN
1566.6-16.5+5.84-3.85
5TomingasTuuliEST
1545.7-30.3+3.80-3.68
6BlashkoDaryaUKR
2044.5-18.3+3.68-3.33
7LieLotteBEL
1872.0-12.8+6.87-3.31
8CadurischIreneSUI
1652.1-18.9+4.13-2.80
9ReidJoanneUSA
1547.5-20.8+3.29-2.74
10HauserLisa TheresaAUT
2611.8-22.8-0.58-2.66
11TodorovaMilenaBUL
1933.9-17.0+2.14-1.84
12OebergElviraSWE
2516.3-9.2-0.11-1.72
13ChevalierChloeFRA
1826.0-18.7+1.25-1.68
14TalihaermJohannaEST
1842.2-12.1+3.10-1.38
15PreussFranziskaGER
2611.5-10.7-0.68-1.34
16SchwaigerJuliaAUT
1937.7-12.8+2.43-1.25
17MaedaSariJPN
1647.4-6.8+3.49-1.07
18CharvatovaLucieCZE
1719.6-5.3+0.50-0.97
19BendikaBaibaLAT
1624.0-9.4+0.85-0.95
20MinkkinenSuviFIN
1867.9-0.9+6.03-0.69
21PerssonLinnSWE
2618.2-4.4+0.16-0.66
22KnottenKaroline OffigstadNOR
2342.7-2.2+3.24-0.65
23GasparinElisaSUI
2145.4-4.8+3.33-0.45
24SimonJuliaFRA
2613.8-0.7-0.55-0.45
25BelchenkoYelizavetaKAZ
1572.3-1.6+7.05-0.40
26TandrevoldIngrid LandmarkNOR
259.3-3.4-0.94-0.39
27GasparinSelinaSUI
1914.4-3.7-0.13-0.30
28KlemencicPolonaSLO
1569.6-2.8+6.56-0.29
29DavidovaMarketaCZE
258.5-2.4-1.05-0.22
30JislovaJessicaCZE
1751.7-1.5+4.02-0.17
31DzhimaYuliiaUKR
2129.8+0.4+1.54-0.08
32MironovaSvetlanaRUS
2220.0+0.8+0.38-0.01
33HettichJaninaGER
2436.4-1.6+2.32+0.02
34RoeiselandMarte OlsbuNOR
265.2-1.9-1.85+0.04
35EganClareUSA
2331.3-0.2+1.95+0.08
36LunderEmmaCAN
2242.0+3.5+2.92+0.14
37HammerschmidtMarenGER
1751.1-0.9+4.26+0.23
38KruchinkinaElenaBLR
2318.8-1.3+0.47+0.25
39EckhoffTirilNOR
264.1-1.4-2.22+0.28
40OebergHannaSWE
2616.8+2.7-0.05+0.34
41GasparinAitaSUI
1950.2+7.0+3.99+0.54
42BescondAnaisFRA
2621.9+4.4+0.73+0.67
43PidhrushnaOlenaUKR
1836.4+7.2+2.33+0.67
44InnerhoferKatharinaAUT
1725.4+7.0+1.03+0.77
45VittozziLisaITA
2526.8+6.8+1.34+0.80
46KryukoIrynaBLR
1540.4+11.9+2.50+0.84
47Braisaz-BouchetJustineFRA
268.3+2.5-1.15+0.86
48EderMariFIN
1719.9+6.4+0.40+1.03
49HinzVanessaGER
2235.1+12.0+2.11+1.07
50TachizakiFuyukoJPN
1855.3+6.7+4.74+1.08
51KuklinaLarisaRUS
1644.5+9.2+3.39+1.12
52WiererDorotheaITA
2617.3+7.2+0.19+1.18
53DunkleeSusanUSA
1946.3+13.3+3.49+1.38
54HaeckiLenaSUI
2131.3+14.3+1.77+1.59
55ZukKamilaPOL
1734.4+13.9+1.99+1.62
56BrorssonMonaSWE
2239.9+12.1+2.98+1.70
57HerrmannDeniseGER
2510.0+7.4-1.02+2.01
58Hojnisz-StaregaMonikaPOL
1634.5+17.5+1.94+2.07
59PuskarcikovaEvaCZE
1860.0+22.9+5.46+2.48

Posted in Statistical analysis | Tagged ski speed

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