real biathlon
    • Athletes
    • Teams
    • Races
    • Seasons
    • Scores
    • Records
    • Blog(current)
    • More
      Patreon Content Course Profiles Explanations Shortcuts
      Error Report
      Privacy Policy About
    •     
  • Forum
  • Patreon
  • Twitter
  • YouTube
    Instagram
    Facebook

Recent Articles

  • Most improved athletes this winter
  • New biathlon point system
  • Historic biathlon results create expectations. But what about points?
  • What do you expect? Practical applications of the W.E.I.S.E.
  • Introducing W. E. I. S. E: the Win Expectancy Index based on Statistical Exploration, version 1

Categories

  • Biathlon Media
  • Biathlon News
  • Long-term trends
  • Statistical analysis
  • Website updates

Archives

  • 2022
    • December
    • June
    • May
    • March
    • February
    • January
  • 2021
    • December
    • November
    • September
    • July
    • June
    • May
    • April
    • March
    • February
    • January
  • 2020
    • December
    • November
    • August
    • June
    • March
  • 2015
    • December
  • 2013
    • August
    • July
  • 2012
    • July

Search Articles

Recent Tweets

Tweets by realbiathlon

Month: January 2021

The Consistency of Consistency tool

Posted on 2021-01-28 | by biathlonanalytics | Leave a Comment on The Consistency of Consistency tool

In biathlon, consistency is something most athletes are looking for, ideally from one season to the next, assuming the performance in a certain metric is at the level they are happy with. I built a dashboard in Tableau Public that looks at the career and seasonal form, averages and variance, and at consistency for the following metrics:

  • Prone Shooting
  • Standing Shooting
  • Total (combined) Shooting
  • Ski speed (in Km/H)
  • Ski Score (Z)
  • Rank
  • Shooting Time Score (Z)
  • Range Time Score (Z)

From the RealBiatlon.com website: Z-score (Standard score) Number of standard deviations by which metrics are above or below the mean (based on back from median data)

The data used goes back to the 2016-2017 season, so when I refer to career averages the data will not include any data from before the 2016-17 season. To highlight this I have used an asterisk whenever using career. Please note that when using different metrics like this, the meaning of above zero and below zero is not always positive or negative. I.e. Z scores for skiing are better when negative (meaning below average) but for shooting percentage the higher number the better.

As examples often are a good way of explaining visualizations I am going to start with Lisa Hauser, and her Ski Score (Z).

Chart 1: Averages

This simply shows Lisa’s average for Ski Score (Z) and the sharp drop for the current season clearly stands out, meaning she went from a just below average skier to a faster than average skier. Also, we can see she has been much faster than her career* average, indicating she must have really focussed on her skiing the last preparation. Has that affected her shooting? Let’s see by changing the metric to Total (combined) Shooting and look at…

Chart 2: Actual Results

This tells us that her current season’s average and her career* average are almost identical, so no change here. We can also see that as the season progresses she is seeing better results (for shooting percentage, higher is better).

Now can we get more out of this? The following shows the difference between actual results and the career* average and shows it cumulatively, based on the assumption the multiple bad results in a row, even with a good result between a number of bad ones, has a bad impact on form.

Chart 3: Cumulative difference for career*

Due to her less than ideal first number of races (with regards to total shooting) and a lesser performance in the last race of the previous season, the chart shows a lower than desired profile, that however sings upward towards the current status of the current season.

One could argue however, that the seasons are separate entities, and the end of last season would not impact the form of an athlete at the start of the current season.

Chart 4: Cumulative difference for season

The same applies in this case for the current season, showing the bad start and the incline due to better results in the second trimester, but the previous season now has no impact at all. A better example of showing a differnece between career* and season is the follwing for Shooting Time Score (Z):

If we want to see more about consistency, the metrics are used in absolute form. It doesn’t matter if a result is good or bad, as long as it differs from the previous results it introduces inconsistency. So the next chart shows the absolute values of the differences between actual race resultes and season averages.

Chart 5: Cumulative absolute difference for season

Now the hight (or depth) of the chart shows the size of inconsistency, where the direction and steepness show how much the race result impacted the consistency.

Lastly to satisfy the more statical inclined readers below are the Variance charts, showing the spread of results and the average Variance per season (still Lias Hauser’s Shooting time score (Z)).

Chart 6: Variance

This dashboard is not coming to a specific conclusion, but rather a tool to further research an athletes’ performances, form, and consistency, intended to be used interactively by you! So go have a look and have fun with it.

Posted in Long-term trends, Statistical analysis | Tagged Tableau, Tool

Ski speed – First vs. second trimester

Posted on 2021-01-28 | by real biathlon | Leave a Comment on Ski speed – First vs. second trimester

The season is already more than halfway over. Who managed to improve their ski form during the winter? In this post, I looked at changes in ski speed for World Cup trimester 1 compared to World Cup trimester 2 (November/December 2020 vs. January 2021). For patrons, I recently updated the comparisons bonus section – there you can compare all shooting and skiing stats on your own, not only season-to-season, but also by trimester now.


Note: Only athletes with at least 5 non-team races in trimester 1 and trimester 2 of the current season 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 trimester).


Men

Surprise podium finisher Felix Leitner managed to improve his ski speed by roughly 1.3% compared to pre-Christmas races. Lukas Hofer improved by virtually the same amount, although he did so on a much higher level (he was the fourth-fastest skier overall for trimester 2). A lot has been made about Johannes Thingnes Bø not dominating as he did in previous seasons, however, his ski speed certainly isn’t to blame; he set the top course time in 5 out of 6 races in January!

Jakov Fak had a great start to his season (four top 10s in a row), but his ski speed is now trending in the wrong direction before the upcoming world championships in Pokljuka. Sebastian Samuelsson‘s speed has declined 1.4% post-Christmas, his average ski rank is 10.9 positions lower. Fabien Claude isn’t doing much better, he also has a ski rank now roughly ten places lower compared to World Cup trimester 1.

Changes in Ski Speed World Cup Trimester 1 vs. World Cup Trimester 2 | 2020–21 season

NoFamily NameGiven NameNationRacesSki Rank
(avg)
Changeback from
Top30 median
(in %)
Change
NoFamily NameGiven NameNationRacesSki Rank
(avg)
Changeback from
Top30 median
(in %)
Change
1LeitnerFelixAUT
619.8-16.6+0.71-1.29
2HoferLukasITA
66.2-7.2-1.51-1.26
3GuigonnatAntoninFRA
620.0-11.0+0.44-1.16
4EliseevMatveyRUS
518.8-17.0+1.07-1.07
5DollBenediktGER
612.5-3.5-0.92-0.92
6FinelloJeremySUI
518.4-9.2+0.26-0.90
7PeifferArndGER
611.5-6.5-0.73-0.90
8BoeJohannes ThingnesNOR
61.3-1.1-3.36-0.70
9DesthieuxSimonFRA
614.7-4.9-0.15-0.60
10EderSimonAUT
634.0-5.2+2.04-0.54
11Fillon MailletQuentinFRA
58.6-2.4-1.11-0.47
12LaegreidSturla HolmNOR
69.7-3.9-0.95-0.41
13KomatzDavidAUT
640.8-5.6+3.18-0.37
14LatypovEduardRUS
616.2-2.7+0.18-0.23
15DaleJohannesNOR
63.0-3.1-1.99-0.02
16JacquelinEmilienFRA
610.5+0.3-0.71+0.10
17BoeTarjeiNOR
66.0+1.4-1.55+0.23
18FakJakovSLO
623.2+3.2+0.71+0.25
19PonsiluomaMartinSWE
67.8+2.2-1.40+0.27
20RastorgujevsAndrejsLAT
522.8+2.2+0.82+0.32
21LesserErikGER
621.5+2.8+0.70+0.34
22WegerBenjaminSUI
624.8+4.6+0.98+0.39
23FemlingPeppeSWE
542.2-9.7+4.06+0.41
24BionazDidierITA
544.6+0.2+3.65+0.45
25ChristiansenVetle SjaastadNOR
518.2+4.9+0.11+0.74
26LoginovAlexanderRUS
521.2+7.5+0.52+0.77
27BocharnikovSergeyBLR
532.6+5.1+1.98+0.92
28ClaudeFabienFRA
619.8+9.6+0.39+1.07
29SamuelssonSebastianSWE
619.2+10.9+0.26+1.39


Women

Elena Kruchinkina, who set the fastest course time in the first Oberhof sprint (before that she never had a top 3 course time), is the most improved skier among regular female starters: 2.4% faster compared to December and her average ski rank is now 16.5 positions lower. Yuliia Dzhima and Svetlana Mironova come second and third, skiing 2.0% and 1.6% faster respectively. Two-time winner Julia Simon improved almost as much percentage-wise, but more importantly, she was the fourth-fastest skier in January.

Monika Hojnisz-Staręga is missing here, because she only appeared in three races in January, however, she would have topped the ranking, 3.4% faster than in trimester 1. Dorothea Wierer‘s skiing has improved slightly, but her average ski rank in January (16.8) is still way behind her average from last season (10.0). Elvira Öberg and Mona Brorsson have been struggling in recent races, both roughly 3% slower in January.

Changes in Ski Speed World Cup Trimester 1 vs. World Cup Trimester 2 | 2020–21 season

NoFamily NameGiven NameNationRacesSki Rank
(avg)
Changeback from
Top30 median
(in %)
Change
NoFamily NameGiven NameNationRacesSki Rank
(avg)
Changeback from
Top30 median
(in %)
Change
1KruchinkinaElenaBLR
510.2-16.5-0.84-2.39
2DzhimaYuliiaUKR
523.0-18.6+0.81-1.96
3MironovaSvetlanaRUS
612.2-13.3-0.57-1.59
4SimonJuliaFRA
66.5-12.8-1.57-1.42
5HettichJaninaGER
627.5-15.3+1.62-1.32
6HinzVanessaGER
530.8-14.2+1.88-1.15
7KaishevaUlianaRUS
625.8-13.3+1.55-1.02
8VittozziLisaITA
521.2-10.2+0.95-0.88
9HerrmannDeniseGER
64.2-4.2-1.90-0.65
10ZdoucDunjaAUT
636.2-15.4+3.16-0.55
11WiererDorotheaITA
616.8-4.3+0.11-0.49
12EckhoffTirilNOR
62.3-3.2-2.38-0.44
13HauserLisa TheresaAUT
610.5-2.2-0.76-0.39
14PavlovaEvgeniyaRUS
524.4-5.1+1.09-0.35
15RoeiselandMarte OlsbuNOR
62.8-1.7-2.24-0.28
16DavidovaMarketaCZE
66.5-0.4-1.29+0.14
17PreussFranziskaGER
612.7+0.6-0.39+0.17
18LunderEmmaCAN
535.8-1.3+2.59+0.18
19BescondAnaisFRA
619.0+3.0+0.34+0.18
20PerssonLinnSWE
623.3+4.0+0.68+0.35
21AlimbekavaDzinaraBLR
615.7+1.4+0.12+0.46
22Braisaz-BouchetJustineFRA
67.8+2.1-1.03+0.59
23OebergHannaSWE
616.7+4.7-0.09+0.62
24Chevalier-BouchetAnaisFRA
613.2+5.7+0.08+1.23
25TandrevoldIngrid LandmarkNOR
520.0+12.9+0.58+1.86
26EganClareUSA
634.7+6.6+3.12+1.88
27KnottenKaroline OffigstadNOR
647.3+14.4+4.56+2.67
28OebergElviraSWE
624.5+17.8+1.25+2.89
29BrorssonMonaSWE
546.6+17.7+4.72+3.17

Overall, the Swedes arguably stand out the most, doing particularly poorly after Christmas – all of their regular starters declined in the new year (often by a lot). Alternatively, you could look at it the other way around: they simply outperformed, especially in Kontiolahti, and now regressed to a more normal level.

Posted in Statistical analysis | Tagged ski speed

Norwegian Dominance

Posted on 2021-01-19 | by biathlonanalytics | Leave a Comment on Norwegian Dominance

The guys from Extra Runde had another great podcast on Monday, in which they talked about Leistungs Dichtheit, which I would translate as Proximity of Performance; how close to each other are athletes from the same nation in the world cup rankings? They looked at the Biathlon World Cup standings and noticed that in the top 15 of the men, there were 5 Norwegians, 33.3% of all athletes, and even almost 40% of the total score for the top 15:

For the women the dominance is not the same, with France, Sweden and Norway all having three athletes in the top 15. But when looking at score percentage, the Norwegians are again ahead:

Historically the Norwegians typically have the majority of the athletes, and often the majortiy of the points. Below I look at the top 30 athletes for men and women by number of athletes and score percentage:

With 16.67% of the athletes and 24.18% of the score, the Norwegians are dominant overall this season. To try your own settings, check the report here, or use the embedded version below:

Cheers!

Posted in Long-term trends | Tagged Norway, World Cup score

Overall performance scores, season-to-season improvements

Posted on 2021-01-19 | by real biathlon | Leave a Comment on Overall performance scores, season-to-season improvements

Last weekend’s mass starts marked the halfway point of the season (13 of 26 races are done). This is probably a good time to take another look at the overall performances this winter. Below, I listed the season-to-season changes in the Overall Performance Score of regular World Cup athletes (at least 8 races in the last two seasons), plus the current top 15 per gender (full results for the entire field here: men & women). You can do your own season-to-season comparisons for all stats in the Patreon bonus area.


Note: The scores are standard scores (or z-scores), indicating how many standard deviations (SD) an athlete is back from the World Cup mean (negative values indicate performances better than the mean). The Total Performance Score is calculated by approximating the importance of skiing, hit rate and shooting pace using the method of least squares (for more details, see here and here), and then weighting each z-score value accordingly.


Men

It’s always interesting to me how accurately this very theoretical method reflects the current World Cup standings. For the men, the top 2, Johannes Thingnes Bø and Sturla Holm Lægreid, are predicted correctly – only through skiing and shooting stats, without taking a single race result into account. I think that’s pretty good. Twelve of the top 15 come within two positions of their current World Cup rank.

Quentin Fillon Maillet is overestimated, in large parts because he forgot to do a penalty loop in the Oberhof sprint (which essentially ruined two races for him). It’s no surprise that something like that isn’t reflected here. Arnd Peiffer is ranked four positions higher than his current World Cup rank, however, he missed two races in Hochfilzen last month and his average race position (14.7) would rank him 9th overall, therefore this ranking is not a bad estimation of his form this season.

Top 15 Overall performance score (z-Scores) | Non-Team events 2020–21 season

NoFamily NameGiven NameNationRacesWorld Cup
Rank
Ski Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
NoFamily NameGiven NameNationRacesWorld Cup
Rank
Ski Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
1BoeJohannes ThingnesNOR
131-2.09-0.65-0.67-1.50
2LaegreidSturla HolmNOR
132-1.45-1.49-0.77-1.38
3Fillon MailletQuentinFRA
127-1.49-1.04-1.14-1.32
4BoeTarjeiNOR
133-1.74-0.85-0.22-1.30
5JacquelinEmilienFRA
136-1.44-1.00-1.26-1.29
6DaleJohannesNOR
134-1.83-0.800.27-1.28
7SamuelssonSebastianSWE
135-1.51-1.05-0.30-1.23
8PonsiluomaMartinSWE
138-1.73-0.11-1.14-1.19
9PeifferArndGER
1113-1.33-1.10-0.53-1.16
10LesserErikGER
1311-1.18-1.00-1.29-1.14
11FakJakovSLO
139-1.09-1.29-0.87-1.12
12HoferLukasITA
1310-1.47-0.46-0.78-1.10
13DollBenediktGER
1312-1.28-0.75-0.97-1.09
14ChristiansenVetle SjaastadNOR
1215-1.32-0.93-0.27-1.08
15LoginovAlexanderRUS
1219-1.31-0.71-0.64-1.06

Looking at changes season-to-season, the Swedish duo Martin Ponsiluoma and Sebastian Samuelsson are the most improved biathletes, mainly due to their new found ski speed. Lukas Hofer comes third; he had a very impressive two weeks in Oberhof (top 6 results in all four non-team races). Benjamin Weger made his first podium in almost eight years last Sunday (his current non-team percentage is at an all-time high, 88.5%); he is the fourth most improved athlete right now.

2020–21 z-Scores compared to 2019–20 | Non-Team events

NoFamily NameGiven NameNationRacesSki Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
Change
NoFamily NameGiven NameNationRacesSki Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
Change
1PonsiluomaMartinSWE
13-1.73-0.11-1.14-1.19-0.69
2SamuelssonSebastianSWE
13-1.51-1.05-0.30-1.23-0.56
3HoferLukasITA
13-1.47-0.46-0.78-1.10-0.44
4WegerBenjaminSUI
13-1.06-0.95-0.54-0.96-0.34
5BocharnikovSergeyBLR
12-1.00-0.39-0.37-0.75-0.33
6SimaMichalSVK
90.24-0.77-0.38-0.13-0.31
7EliseevMatveyRUS
13-0.82-1.15-1.10-0.95-0.28
8GowChristianCAN
11-0.36-1.16-0.68-0.63-0.27
9LatypovEduardRUS
13-1.14-0.360.08-0.77-0.26
10HasillaTomasSVK
80.190.38-0.030.22-0.26
11FakJakovSLO
13-1.09-1.29-0.87-1.12-0.25
12SmolskiAntonBLR
11-1.17-0.24-0.11-0.77-0.21
13DaleJohannesNOR
13-1.83-0.800.27-1.28-0.21
14EderSimonAUT
13-0.50-1.64-1.33-0.93-0.15
15JacquelinEmilienFRA
13-1.44-1.00-1.26-1.29-0.14
16KrcmarMichalCZE
9-0.83-1.100.03-0.80-0.11
17LangerThierryBEL
9-0.22-0.360.47-0.17-0.11
18PeifferArndGER
11-1.33-1.10-0.53-1.16-0.11
19BormoliniThomasITA
8-0.72-0.74-0.41-0.69-0.10
20ChristiansenVetle SjaastadNOR
12-1.32-0.93-0.27-1.08-0.10
21NordgrenLeifUSA
9-0.31-0.52-0.21-0.36-0.10
22ClaudeFabienFRA
13-1.37-0.01-0.85-0.92-0.10
23NelinJesperSWE
11-1.400.240.01-0.75-0.10
24BoeTarjeiNOR
13-1.74-0.85-0.22-1.30-0.08
25DombrovskiKarolLTU
10-0.14-0.820.48-0.26-0.08
26DollBenediktGER
13-1.28-0.75-0.97-1.09-0.06
27DovzanMihaSLO
90.40-0.69-1.24-0.11-0.06
28WindischDominikITA
9-0.94-0.04-0.29-0.60-0.05
29RastorgujevsAndrejsLAT
12-1.14-0.06-0.30-0.73-0.04
30GuigonnatAntoninFRA
13-0.90-0.80-0.43-0.82-0.02
31LeitnerFelixAUT
11-0.93-0.730.73-0.68+0.00
32SeppalaTeroFIN
11-1.000.07-0.02-0.57+0.03
33Fillon MailletQuentinFRA
12-1.49-1.04-1.14-1.32+0.04
34DohertySeanUSA
11-0.34-0.36-0.60-0.38+0.08
35YaliotnauRamanBLR
10-0.920.59-0.03-0.37+0.08
36LoginovAlexanderRUS
12-1.31-0.71-0.64-1.06+0.10
37PrymaArtemUKR
11-0.73-0.24-0.87-0.60+0.10
38ErmitsKalevEST
90.020.27-0.110.07+0.11
39BoeJohannes ThingnesNOR
13-2.09-0.65-0.67-1.50+0.11
40StroliaVytautasLTU
10-0.42-0.110.16-0.26+0.11
41FemlingPeppeSWE
12-0.19-0.39-1.04-0.35+0.14
42IlievVladimirBUL
8-0.880.25-0.07-0.45+0.14
43DesthieuxSimonFRA
13-1.20-0.51-0.80-0.95+0.15
44EberhardJulianAUT
8-1.130.05-0.30-0.69+0.17
45MoravecOndrejCZE
10-0.34-0.77-0.75-0.51+0.20
46PidruchnyiDmytroUKR
11-0.69-0.17-1.19-0.60+0.20
47StvrteckyJakubCZE
10-0.981.081.28-0.11+0.21
48GaranichevEvgeniyRUS
8-0.42-0.93-0.46-0.58+0.22
49KuehnJohannesGER
9-1.23-0.040.57-0.67+0.24
50ClaudeFlorentBEL
9-0.48-0.110.92-0.21+0.25
51TrsanRokSLO
80.63-0.74-0.920.05+0.34
52GowScottCAN
90.14-0.11-1.12-0.08+0.38
53GuzikGrzegorzPOL
10-0.060.73-0.460.12+0.38


Women

World Cup leader Marte Olsbu Røiseland tops the women’s ranking, even though Tiril Eckhoff has won 6 out of 13 races so far. It’s a bit surprising that Eckhoff is only ranked third. Her overall season statistics are still affected by her very poor season opener in Kontiolahti, where she failed to make the World Cup points in both races.

Eckhoff’s ranking could be an argument that the best and worst one/two/three data points should be thrown out to better reflect an athlete’s expected standard performance. Presumably, the ranking would then no longer correlate as closely to the current World Cup standings though. Overall, 13 of the top 15 come within two positions of their current World Cup rank; no one in the top 8 is off by more than one position.

Top 15 Overall performance score (z-Scores) | Non-Team events 2020–21 season

NoFamily NameGiven NameNationRacesWorld Cup
Rank
Ski Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
NoFamily NameGiven NameNationRacesWorld Cup
Rank
Ski Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
1RoeiselandMarte OlsbuNOR
131-1.51-0.88-1.18-1.29
2OebergHannaSWE
133-1.20-0.97-1.82-1.21
3EckhoffTirilNOR
132-1.51-0.64-0.53-1.14
4WiererDorotheaITA
134-0.94-1.26-1.56-1.10
5OebergElviraSWE
136-1.32-0.64-0.80-1.06
6PreussFranziskaGER
135-1.17-0.73-1.24-1.05
7HauserLisa TheresaAUT
138-1.15-0.73-1.03-1.02
8AlimbekavaDzinaraBLR
137-1.12-1.02-0.42-1.01
9SimonJuliaFRA
1312-1.19-0.12-1.75-0.94
10Braisaz-BouchetJustineFRA
1314-1.39-0.31-0.14-0.93
11DavidovaMarketaCZE
139-1.38-0.35-0.07-0.93
12HerrmannDeniseGER
1311-1.38-0.16-0.52-0.93
13TandrevoldIngrid LandmarkNOR
1213-1.29-0.51-0.05-0.91
14PerssonLinnSWE
1315-0.98-0.78-0.25-0.84
15KnottenKarolineNOR
1316-0.47-1.31-1.30-0.81

Just like on the men’s side, the most improved skier, Dzinara Alimbekava, is also the most improved biathlete overall, confirming again that ski speed is by far the most important aspect of the sport – maybe not in a single race, but certainly if you look at results over longer periods. Elvira Öberg and Tuuli Tomingas come second and third. Monika Hojnisz-Staręga suffered one of the biggest declines season-to-season, however, after an extremely poor December, she was much improved in Oberhof.

2020–21 z-Scores compared to 2019–20 | Non-Team events

NoFamily NameGiven NameNationRacesSki Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
Change
NoFamily NameGiven NameNationRacesSki Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
Change
1AlimbekavaDzinaraBLR
13-1.12-1.02-0.42-1.01-0.93
2OebergElviraSWE
13-1.32-0.64-0.80-1.06-0.54
3TomingasTuuliEST
10-0.66-0.390.16-0.48-0.54
4ZdoucDunjaAUT
13-0.33-1.07-0.30-0.54-0.38
5HettichJaninaGER
13-0.50-1.26-0.06-0.67-0.31
6ReidJoanneUSA
9-0.46-0.510.77-0.33-0.30
7KnottenKarolineNOR
13-0.47-1.31-1.30-0.81-0.28
8ColomboCarolineFRA
11-0.76-0.29-0.49-0.59-0.26
9HauserLisa TheresaAUT
13-1.15-0.73-1.03-1.02-0.23
10MinkkinenSuviFIN
90.03-0.59-0.92-0.27-0.20
11MironovaSvetlanaRUS
10-1.06-0.18-0.81-0.78-0.19
12BlashkoDaryaUKR
10-0.31-1.320.17-0.55-0.16
13KlemencicPolonaSLO
80.070.36-0.060.14-0.14
14SolaHannaBLR
11-1.130.95-0.97-0.51-0.14
15DavidovaMarketaCZE
13-1.38-0.35-0.07-0.93-0.14
16LunderEmmaCAN
13-0.52-1.02-1.15-0.74-0.13
17Braisaz-BouchetJustineFRA
13-1.39-0.31-0.14-0.93-0.08
18OebergHannaSWE
13-1.20-0.97-1.82-1.21-0.07
19RoeiselandMarte OlsbuNOR
13-1.51-0.88-1.18-1.29-0.05
20SchwaigerJuliaAUT
10-0.60-0.59-0.01-0.53-0.05
21SimonJuliaFRA
13-1.19-0.12-1.75-0.94-0.05
22MaedaSariJPN
9-0.490.600.21-0.09-0.05
23TodorovaMilenaBUL
10-0.610.010.20-0.33-0.03
24BrorssonMonaSWE
11-0.72-0.77-0.62-0.72-0.03
25PerssonLinnSWE
13-0.98-0.78-0.25-0.84-0.03
26TandrevoldIngrid LandmarkNOR
12-1.29-0.51-0.05-0.91-0.01
27HaeckiLenaSUI
11-0.70-0.17-1.61-0.66-0.01
28KryukoIrynaBLR
8-0.79-0.860.97-0.60+0.00
29WiererDorotheaITA
13-0.94-1.26-1.56-1.10+0.01
30GasparinElisaSUI
10-0.55-0.12-0.84-0.46+0.02
31PreussFranziskaGER
13-1.17-0.73-1.24-1.05+0.02
32EckhoffTirilNOR
13-1.51-0.64-0.53-1.14+0.03
33TachizakiFuyukoJPN
10-0.32-0.520.64-0.27+0.05
34KruchinkinaElenaBLR
13-0.91-0.210.87-0.49+0.06
35ChevalierChloeFRA
11-0.930.000.75-0.46+0.07
36JislovaJessicaCZE
9-0.400.040.27-0.19+0.08
37FrolinaAnnaKOR
9-0.160.44-0.36-0.01+0.10
38GasparinAitaSUI
10-0.38-0.59-0.85-0.50+0.10
39ZukKamilaPOL
10-0.810.021.11-0.34+0.10
40SkottheimJohannaSWE
11-0.41-0.96-0.96-0.64+0.10
41BescondAnaisFRA
13-1.03-0.400.19-0.70+0.11
42GasparinSelinaSUI
9-1.100.200.18-0.57+0.11
43EderMariFIN
9-1.170.281.25-0.46+0.11
44BelchenkoYelizavetaKAZ
80.41-0.59-0.120.05+0.12
45HinzVanessaGER
10-0.58-0.86-0.33-0.63+0.12
46CharvatovaLucieCZE
9-1.031.50-0.93-0.28+0.13
47EganClareUSA
13-0.73-0.500.66-0.50+0.14
48HerrmannDeniseGER
13-1.38-0.16-0.52-0.93+0.15
49TalihaermJohannaEST
9-0.25-0.190.93-0.09+0.15
50DzhimaYuliiaUKR
10-0.74-0.66-0.20-0.65+0.17
51PidhrushnaOlenaUKR
8-0.58-0.37-0.20-0.48+0.20
52PuskarcikovaEvaCZE
10-0.11-0.46-1.25-0.35+0.20
53VittozziLisaITA
12-0.74-0.06-0.64-0.53+0.23
54DunkleeSusanUSA
10-0.470.29-0.14-0.21+0.23
55Hojnisz-StaregaMonikaPOL
8-0.61-0.91-0.29-0.66+0.30
56InnerhoferKatharinaAUT
10-0.991.31-0.00-0.20+0.40
57ZbylutKingaPOL
10-0.070.360.000.07+0.42

Posted in Statistical analysis | Tagged 2020–21 season, overall performance, shooting, skiing

“Whether the weather is better or worse, the race is still always made on the course”

Posted on 2021-01-13 | by biathlonanalytics | Leave a Comment on “Whether the weather is better or worse, the race is still always made on the course”

In August of last year, I wrote an article on this website about “Impact of external factors on shooting performance in biathlon“. I was still limited to using hand-scraped race data of women’s sprint races only and had to work with the restrictions of using weather data of which the quality was unknown but likely not very high (“all these measurements should be taken with a grain of salt; how accurately are they measured, it’s only on one measure location, some measurements are qualitative”). For shooting performance, I used Shooting time + Penalty time, and I came to the conclusion that for impact on shooting performance the most important indicators are the combination of maximum wind speed and change in speed and visibility, and that course conditions had limited impact.

Then on RealBiathlon the article “Is Oberhof the most challenging venue on the World Cup tour?” appeared at the end of last year, which looked at venue hit rates and average shooting times over the years, as well as venue course difficulty and median ski speed, and provided this data to its subscribers. From this data, I only used venues that were still in use in the 2016-2017 to the current season period, and that had 40 races or more:

This clearly shows basically for all factors (ski speed, shooting percentage, and shooting time) that Oberhof is the least favourable venue for athletes from a performance perspective.

For weather, I’m going to focus on wind specifically. Due to the qualitative and somewhat inconsistent weather data I don’t feel comfortable enough about this data to draw any conclusions (as I also concluded in August). Here are some combinations of Sky values at the start, after the start and at the finish. Depending on when athletes start they can have very different experiences, and remember the sky value comes from one location at the venue.

So let’s look at wind again then, now that we have a lot more and better data for men and women, all non-team races and going back to the 2016-2017 season:

As with the previous analysis we can see from the above that Wind Strength correlate strongest with Shooting performance, with roughly 12% of the change in shooting Performance being attributed to the maximum wind strength, and about 6% to the change in wind strength. The wind direction has no statistically significant impact on the shooting performance.

The table below compares the measured values from August 2020 to this article. There are some changes, but the top two variables remains he ones statistically significant although their impact changes somewhat:

CorrelationAugust 2020Jan. 2021
Max. Wind Strength – Shooting PerformanceR2=0.356R2=0.121
P=0.0017P<0.0001
Change in wind Strength – Shooting Perf.R2=0.043R2=0.063
P=0.043P=0.0006
Change in Wind direction – Shooting Perf.R2=0.3R2=0.0015
P=0.189p=0.603

Also the same as in August is that the correlation between Maximum Wind Strength and Change in Wind Strength is strong, be it a little less at 61% but that the Change in Wind Direction does not correlate much with the Maximum Wind Speed (just over 2% with a significance of just below 5%).

If we plot the average change in wind speed (vertical) and average maximum wind speed (horizontal) for all locations since the 2016-2017 (I lexcluded PyeongChang, Sodier Hollow and Tyumen as they are not regular event locations) we can see which venues have tough wind-conditions, and – as we know now – have tough shooting conditions:

In some cases there is clear overlap with the chart shown at the beginning of the article (Oberhof) but almost all venues do not align between the two charts. This is where we need to remind ourselves we are talking about 36% impact at the most, which leaves 64% impact for other variables.

In the end I was happy to see the wind charts that now used much more data produced similar results, but dealing with weather data remains risky when it comes to drawing any conclusions.

Posted in Statistical analysis | Tagged weather

Posts navigation

Older posts

Recent Articles

  • Most improved athletes this winter
  • New biathlon point system
  • Historic biathlon results create expectations. But what about points?
  • What do you expect? Practical applications of the W.E.I.S.E.
  • Introducing W. E. I. S. E: the Win Expectancy Index based on Statistical Exploration, version 1

Categories

  • Biathlon Media
  • Biathlon News
  • Long-term trends
  • Statistical analysis
  • Website updates

Archives by Month

  • 2022: J F M A M J J A S O N D
  • 2021: J F M A M J J A S O N D
  • 2020: J F M A M J J A S O N D
  • 2015: J F M A M J J A S O N D
  • 2013: J F M A M J J A S O N D
  • 2012: J F M A M J J A S O N D

Search Articles