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Category: Statistical analysis

Who were the best performing biathletes at the Beijing Olympics?

Posted on 2022-03-02 | by biathlonanalytics | Leave a Comment on Who were the best performing biathletes at the Beijing Olympics?

Introduction

This article accompanies the Tableau Public dashboards I created to highlight those athletes who performed better than their season average at the Olympic Winter Games in Beijing, and look at those below their average.

Data

The data used for this analysis are all from the race analysis reports from the non-team IBU races in the 2021-2022 season up to and including the Olympic Winter Games in Beijing. The data was then split into two groups. The Olympic Games races, and the races during the first two trimesters of the season. After averaging the performances per group, the two groups were then compared.

I would like to note that the data for the Olympic games is based on four races or less. This is a very small sample size to use for averages that show Olympic performances. While some of these performance differences can be explained by (bad) luck on an individual level, at the nation or gender level the averages will eliminate or at least significantly reduce this luck factor.

Performances

This analysis looks at which athletes over- or underperformed compared to their statistics in the first two trimesters of the IBU World Cup, rather than at their overall performance at the Olympics. For example, while Justine Braisaz-Bouchet went home with a gold medal, on average she was slower and shot a lower percentage than her first two trimesters.

The Skiing performance is expressed in the average seconds behind the leader per 1,000m. The calculation uses the total course length as provided on the IBU Biathlonresults.com webpage. Please note that by using this metric we also get a sense of how much the field was spread out, as it looks at the seconds behind the leader.

The Shooting performance uses the average total shooting percentage (prone and standing combined).

As the values for both skiing and shooting performances differences were in the same range I added a Combined difference of the two. A negative skiing performance and positive shooting performance are considered improvements. Therefore the calculation is [Shooting performance] – [Skiing performance].

Field

Before jumping into the individual results it is a good idea to look at the averages for men (blue circle), women (orange triangle) and everyone combined:

From these numbers, we can assume that both the skiing conditions as well as the shooting conditions were tougher than the average conditions during the first two trimesters as all athletes combined were (on average) 2.4 sec./1,000m slower and shot 1.7% worse. This aligns with what we have seen and read about the Olympic races being very tough.

Tableau dashboards

I encourage you to have a look at the table and charts on Tableau Public page I created for this analysis. It allows you to filter the data and will show you additional information by hovering over the data points. It will also allow you to see more details and information than described below. The screenshots used in this article are taken from the same dashboards.

Link to Tableau Public page

Men

When looking at the men’s performances for all athletes that raced all four non-team events (you can change this in the interactive dashboards), the Canadian Jules Burnotte had the best performance improvement during the Olympics. He had basically the same ski speed and shot 7.25% higher than in the first two trimesters. Martin Ponsiluoma, Dominik Windisch, Roman Rees, Quentin Fillon Maillet and Tarjei Boe were also better (on average) compared to their first two trimesters.

Sturla Holm Laegreid on the other hand had the worst performance, seeing his shooting percentage drop by over 18% and skiing just a tiny bit slower. Emilien Jacquelin and Alexandr Loginov probably also had hoped for better individual performances.

There were only two male athletes with four races that improved their skiing performance: Artem Pryma from Ukraine, and Johannes Thingnes Boe from Norway. And they only shaved off 0.6 and 0.2 seconds per 1,000m respectively (but keep in mind the average for men was 2.9 seconds/1,000m slower). Emilien Jacquelin, Sebastian Samuelsson and Felix Leitner lost the most speed, with more than five seconds/1,000m.

When looking at all athletes, regardless of how many races they participated in, Raido Raenkel from Estonia (3 races) was 1.14 sec./1,000m slower but shot 17.5% better, for a combined improvement of 16.36, the best of the field. He was closely followed by Matej Baloga and Sebastian Stalder.

Women

On the women’s side, the best performance improvement came from Katharina Innerhofer from Austria (only including athletes with all four races). She was almost a second faster and shot 15% better. Yuliia Dzhima, Paulina Fialkova, Lucie Charvatova, Tiril Eckhoff, Denise Herrmann, Marte Olsbu Roeiseland, Deedra Irwin and Elvira Oeberg were the other female athletes who improved compared to the first two trimesters.

Hanna Oeberg, Uliana Nigmatullina and Anias Bescond had the largest combined decrease in performance.

There were actually 13 athletes who improved their skiing performances during the Olympics, with not the much-discussed athlete Tiril Eckhoff making the biggest improvement, but Deedra Irwin (3.8 sec./1,000m) from the USA. Lucie Charvatova on the other hand lost just over 5 sec./1,000m.

The same Charvatova, and Kathariana Innerhofer improved their shooting the most, by 12.4 and 15% respectively, while Anias Bescond, Hanna Sola and Uliana Nigmatullina had the biggest drop in shooting percentage (13, 14 and 17.3%).

For all athletes, ignoring the number of races they participated in, the best improvement was by Maria Zdravkova from Bulgaria (2 races), who was 0.45 sec./1,000m faster and shot 17.5% above her pre-Olympic season average. Innerhofer (4 races) and Erika Janka from Finland (1 race) were close behind.

The athletes in the top right corner improved both in skiing and shooting. The bottom left corner has a decrease in both:

Nations

The performance improvement results for nations, split by gender, are based on all athletes that participated for a nation in non-team events, regardless of the number of races they participated in. This should be kept in mind when looking at nations like Denmark&Greenland women and New Zealand men (2 races total for both), or Sweden (women) and Germany (men) with 16 races. For the following paragraphs, I only looked at nations that had 8 or more races in total (half of the max. number of races possible).

Since we are only looking at the nation’s average, the results don’t say anything about how spread out the individual results were within the team, and this can strongly vary between teams. The averages were calculated by averaging all the nation’s athlete’s race results, rather than averaging the athlete’s averages per nation and gender.

Women

The top 6 nations on the women‘s side all had an improvement: Ukraine, Norway, Slovakia, USA, Japan and Finland. All other nations had a decrease in combined performance, with France having the worst combined performance improvement followed by Canada, Russia, Belarus and China. Norway improved most in skiing, and Ukraine in shooting.

Men

Moving over to the men‘s side, there were only four nations that improved on their combined performance: Slovakia, China, Canada and Switzerland. The worst combined performances were from Belgium, Finland, Belarus, USA and Russia. The biggest improvers in skiing were China, Canada, Russia and Norway, while Slovakia, China Switzerland, Estonia, Canada, Bulgaria and Italy were the only nations that improved their shooting performance.

When we look at a combined overview we can see that overall women made bigger improvements than men:

Again, I encourage you to check out these visuals interactively on Tableau Public, specifically the ones used above: Athletes Table, Athletes Chart and Nations Chart.

Cheers!

Posted in Statistical analysis | Tagged Beijing 2022, Olympic Winter Games

Individual Olympic gold medals in biathlon (1960 – 2022)

Posted on 2022-02-19 | by real biathlon | Leave a Comment on Individual Olympic gold medals in biathlon (1960 – 2022)

Individual/non-team Olympic titles in biathlon – updated (1960 – 2022)
Complete record list:
https://www.realbiathlon.com/record

Posted in Biathlon Media, Long-term trends, Statistical analysis | Tagged 2022 Winter Olympics

Rallenta Lisa; the athlete with 2 faces

Posted on 2022-02-09 | by biathlonanalytics | Leave a Comment on Rallenta Lisa; the athlete with 2 faces

Introduction

When watching her today, it is hard to imagine Lisa Vitozzi was fighting for the crystal globe just three seasons ago. She regularly shows flashes of fast skiing and good shooting. But unfortunately, those performances go paired with terribly bad shootings. Especially the first shooting, in prone, has been an incredibly low 41% this season. Yet, when she shows up for the first shooting of a relay race, we see a completely different athlete, shooting 84%.

Of course, shooting in a relay is not the same as shooting in non-team events due to the additional three bullets. But although one could argue relay shooting becomes easier due to this fact, another argument can be made that athletes may take more risk, as they have three bullets to spare.

Having shot four or more misses in her first shootings in the last six non-team events will have a major impact on her mindset. By now I can only imagine it’s the one thing she doesn’t want to think of, but will regardless, especially if she misses the first shot. But I wondered if there was more to it than just the mental aspect. Perhaps a different tactic has had an impact as well? In this article, I research if the data shows that there is more to Vitozzi’s demise in the non-team events than her mental state alone.

Data

I started with data for the 2020-2021 season and the current season to date, including the Individual at the Olympic Games in Beijing. In this timespan, Vitozzi participated in 39 non-team events and 11 relays, for which I analyzed all shots in her first shootings. Well, not exactly all shots, as a shooting percentage for relays typically includes spare bullets that were used as well.

For relays, I only looked at the first five shots and calculated the shooting percentage for those five shots only, knowing that this is not a completely fair comparison due to the above-mentioned difference. Also, 11 relay races is a small sample size, but I chose not to go back further in time as this mostly appears to be an issue of the current season.

Goal

What I was curious about was if her skiing tactics play a role in her shooting problems. To be more precise, does she shoot worse because she is pushing harder in the first lap in non-team events compared to the relays? After all, it is pretty common to see the first lap of a relay go at a pace that reminds us more of a warmup lap.

Since weather conditions, course profiles, types of snow and elevation all have a significant impact on skiing, I couldn’t just compare lap times. So I did the following: I looked at Vitozzi’s lap times in a race (based on course time) and compared the first lap to the average of all her laps in that race. This gave me an indication of her first lap being faster or slower than her average time on the course.

Visualization

The following chart shows all of Vitozzi’s races in the current season so far, represented by a coloured dot. They are ordered by date on the horizontal axis, with the older races on the left and the most recent on the right. The vertical axis shows how the course time of the first loop relates to the average course time of all loops. Below 100% is a faster loop, above 100% a slower one. The labels show her shooting percentage for the first shooting of that race.

A couple of things stand out: in four out of five relays this season she started slower than her average, she shot 80% or better in her first shooting. For the one relay she skied a faster first lap she shot 60%. With a few exceptions, in the races where she starts slower, she hits four out of five. Where she starts faster, she misses between three and five shots.

Now if we look at all the races from the dataset, combined with the averages for discipline, the story the data tells is not much different.

The relays, in which on average she starts her first lap slower than her average lap time, she shoots between 85 and 90%. But in all the other disciplines she starts faster (on average) than her average lap time and shoots worse.

Conclusion

It is clear that Vitozzi’s issue is going to take a lot of mental healing before we see any improvement for her on the range. And we need to be careful not to draw too firm a conclusion while using averages on small sample sizes. But considering that Vitozzi is probably looking for anything to help her right now, slowing down on her first lap may be another factor that can contribute to her getting back to the level we all know she is capable of. Rallenta Lisa!

Posted in Statistical analysis

Cool ways to measure and display ski speed in biathlon

Posted on 2022-02-04 | by biathlonanalytics | Leave a Comment on Cool ways to measure and display ski speed in biathlon

Introduction

The ski speed in biathlon is measured and displayed in many different ways. This article makes an attempt to review those different ways and come up with a “best way” to display ski speed, as I believe that some of the current options make ski speed hard to understand. The goal is to have a clear measure and display unit that is understandable to both die-hard biathlon fans as well as the occasional biathlon watcher.

It is also important to know that I looked at these different options from both a per-race and a seasonal-average perspective.

Lastly, I want to emphasize that whatever I end up with, none of the options is bad or wrong, and I definitely don’t want to claim I have all the knowledge to make a final decision on what is best. This is my take, and I look forward to further discussing this topic.

Data

One way or the other, all the data eventually comes from the IBU data center. Here, the IBU provides race times, ski times and course times in different formats but all from the same source. Their data is tracked and collected by Siwidata by the use of trackers that all athletes get wrapped around their ankles and data collection points along the track.

Time data

The competition analysis report, available directly from the IBU data center, combines all the time information per athlete per loop and combined:

Course data

Also important for some calculations described further down in this article is that we have a total course length for the event, and this includes the skiing tracks as well as the range section:

Unfortunately, we do not have data for just the skiing part of the track versus the range part of the track. But when we take into consideration that there typically are 30 lanes of 2.75-3m width, and the range includes 10m on either end, we can guesstimate that the range length is about 110m long. This is a very small part of the total course length (even for a sprint it is 1%). And since we don’t have a better alternative, the total course length is used in the calculations for some of the measures further down in this article.

Course map

The following image clarifies what parts of the racetrack are considered course time, range time and penalty time:

It should be noted that a small section of the penalty loop overlaps with the “normal” track, which explains why every athlete will have a couple of seconds of penalty time per loop, even if they shoot clean. Important to note here is that for ski speed in biathlon, we use only the course time data.

Time data, part II

All races have data for the total race time and the course total time, as well as the penalty time for all races except the individual race discipline which uses the ski time:

  • Race time (above with the header “Finish”) typically called Loop time is the total time from start to finish, including range and penalty time;
  • Ski time is the Race time minus the one-minute penalty times in individual races (other race disciplines do not have ski time data);
  • Course time is the total time skiing, excluding time on the range and in the penalty loop. This is the data used for measuring how fast the athletes were skiing.

Measurements

The following is a list of measurements based on the data described above, currently used in the “biathlon world”. I try to give some pros, cons and comments on each of them.

  • Seconds behind: absolute measure of the distance between athletes in seconds behind the fastest skier at the finish line
    • Good way to show actual time distance between the athletes at the finish
    • Doesn’t work well for aggregating multiple race disciplines as 10 seconds behind on the sprint is different than 10 seconds behind on the individual
  • Seconds behind leader per 1,000 meter: absolute measure of the distance between athletes in seconds behind the fastest skier per 1,000 meters
    • This normalizes the distance between athletes in seconds so it can be aggregated between multiple race disciplines
    • Uses total course length to calculate
    • Used by the IBU in biathlon information
  • Seconds behind per penalty loop: absolute measure of the distance between athletes in seconds behind the fastest skier per 150 meters, the length of a penalty loop
    • Same as previous but normailzes to a distance people, both die-hard and occasional fans, can easily relate to
    • Can be aggreated between multiple race disciplines
    • Numbers can get very small
    • Uses total course length to calculate
  • Per cent behind, or per cent back: relative measure of the distance between athletes in a percentage of the ski time of the fastest skier
    • Fastest athlete is always 0%
    • There is no range for percentages; the slowest in one race can be +33% where in other races it can be 50%
  • Per cent of average ski speed (of the whole field): relative measure of the distance between athletes in a percentage of the average ski time of all skiers
    • Average ski time (0%) includes every athlete from best to worst
    • You don’t really know how fast or slow someone is, as you don’t know the minimal and maximum percentage values
    • The negative values (-3.4%) represent being faster than average so a positive result
  • Per cent of the average of top 5/10/30 skiers: relative measure of the distance between athletes in a percentage of the average ski time of the top 5/10/30 skiers
    • Average ski time can be focussed only on top 5/10/30 skiers
    • This can level the field when comparing sprints (over 100 athletes) to mass starts (30 athletes)
  • Meters behind leader: absolute measure of the distance between athletes in meters behind the fastest skier on the total course length at the finish line
    • Apparently the way Siwidata now measures for the IBU
    • Doesn’t work well for averaging multiple race disciplines as 10 meters behind on the sprint is different than 10 meters behind on the individual for example
  • Meters behind leader per penalty loop: absolute measure of the distance between athletes in meters behind the fastest skier per 150 meters, the length of a penalty loop
    • Same as previous but normailzes to a distance people, both die-hard and occasional fans, can easily relate to
    • Can be aggreated between multiple race disciplines
    • Numbers can get very small
    • Uses total course length to calculate
  • Ski speed: absolute measure of the difference between athletes in kilometres per hour
    • Doesn’t say much about how the speed relates to time difference between athletes
    • Has different effect on race depending on the race distance
  • Ski speed rank: absolute measure of the difference between athletes in the rank in ski speed
    • Fastest is rank 1, slowest is the highest number
    • Downside is that it loses the actual distance between racers. One can be the third ranked skier by two seconds behind the leader or two minutes behind the leader
  • Zscore: computed measure of the difference between athletes in the number of standard deviations by which course times are above or below the mean, based on seconds behind from median of all athletes
    • Can be hard to relate to by all fans
    • Although a precise value, only gives a general sense of someone being faster or slower than the field mean
    • The negative values (-3.4%) represent being faster than average so a positive result
  • Course time: absolute measure of the difference between athletes in the actual course times
    • Gives a good idea of how long the athletes took to ski the track
    • Need to calculate the actual differences
  • Time behind score: relative measure of the difference between athletes on a 0-100% range, where fastest athlete is 100% and slowest is 0%
    • Further explanation can be found in this post

Ways of communicating

Tables

All measures can be shown in a table, which provides a detailed overview of the race results per that specific measure. They are great for looking up specific athletes and giving the exact numbers, but they are harder to interpret quickly and to envision the fastest skiers and how far they are from each other.

Charts

Charts on the other hand are easy to interpret quickly while still proving detailed information per individual athlete (especially when created interactively), and context (see example at end of article) to make it even easier to read.

Description/talking

Probably the most complicated but least considered aspect of communicating ski speed is how it can be discussed. What does it mean when someone says Laegreid was -2,6% from the average, Latipov was 13 seconds behind, Boe was 16 meters behind, Lesser was 3% back, and so on? For die-hard biathlon fans this may make sense for those who are “into data”. For those who just love to watch biathlon and casual fans, I believe this way of describing ski speed is not very meaningful or useful.

Rankings are clear in the sense to discuss who was faster and slower, but not how much faster or slower. Measures per a relatable distance, like a penalty loop, are easy to understand and visualize for biathlon fans at all levels. They don’t even need to know the actual distance of a penalty loop!

Conclusion

When we take a look at most of the measurements in chart format, we can see that the majority show the same data but just with a different axis and units (the red, orange and yellow icons indicate rank 1, 10 and 30 respectively). And with all charts being equal we can decide which unit of measurement would be the easiest to communicate to all types of biathlon fans while having the ability to aggregate the data for a whole season.

I already mentioned that although Seconds behind, Meters behind leader, and Course time are easy to relate to, they are not ideal for aggregation as race disciplines have different distances. The opposite is the case for Per cent behind, Per cent of average ski speed, and Zscore, as they aggregate well but are not so easy to relate to for all fans. The Ski speed in Km/h is cool in the sense that it makes you realize how fast these athletes go. But it doesn’t say much about the end result (difference between athletes) and it would be hard to aggregate.

Ski speed rank shows a different picture of the data when we put it in a chart. The ski speed rank is easy to relate to and very clear to communicate and aggregate, but it loses the information about the space between athletes.

For both single races and season aggregations, the Meters behind leader per penalty loop is a measure that is easy to understand for any biathlon fan, can be aggregated for a whole season with different race disciplines as it is normalized to a specific distance (150 meters), and it keeps the information on space between athletes intact. And on top of that, it can be visualized in cool ways.

It is also very similar to what the IBU currently uses (seconds behind per 1km) but I think that meters behind are easier to visualize mentally and understand than seconds behind and that it is good to use a distance people can relate to directly. The only downside to using the 150-meter loop is that athletes do appear very close to each other.

What is your preferred way of measuring ski speed? And do you agree or disagree with my comments above? Let’s have a conversation on Twitter, I’d love to hear other people’s perspectives.

Posted in Statistical analysis | Tagged ski speed

Olympic Mixed Relay Projection

Posted on 2022-02-04 | by real biathlon | Leave a Comment on Olympic Mixed Relay Projection

Who are the favorites for the opening biathlon event at 2022 Winter Olympics? Here are the overall relay performances scores for the top 10 nations in the Mixed Nations Cup score (team performances this season).


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.


Norway – Average Performance Score: -1.16

NoFamily NameGiven NameNationRacesRank
(avg)
Points
(avg)
Ski Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
1RoeiselandMarte OlsbuNOR
34.046.3-1.35-1.24-1.86-1.53
1EckhoffTirilNOR
34.046.3-1.520.050.57-0.54
1BoeTarjeiNOR
41.060.0-1.06-1.06-0.77-0.95
1BoeJohannes T.NOR
41.060.0-1.93-1.59-1.17-1.60

France – Average Performance Score: -1.06

NoFamily NameGiven NameNationRacesRank
(avg)
Points
(avg)
Ski Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
NoFamily NameGiven NameNationRacesRank
(avg)
Points
(avg)
Ski Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
2Chevalier-BouchetAnaisFRA
44.049.5-1.280.44-1.66-1.22
2SimonJuliaFRA
31.756.0-1.26-0.37-1.07-1.08
2JacquelinEmilienFRA
33.049.3-1.20-0.410.27-0.55
2Fillon MailletQuentinFRA
33.049.3-1.49-1.47-1.24-1.39

Belarus – Average Performance Score: -0.95

NoFamily NameGiven NameNationRacesRank
(avg)
Points
(avg)
Ski Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
3AlimbekavaDzinaraBLR
45.544.0-1.23-1.24-1.71-1.41
3SolaHannaBLR
56.241.6-1.460.420.50-0.49
3LabastauMikitaBLR
45.842.3-0.88-0.74-1.15-0.96
3SmolskiAntonBLR
55.841.4-1.09-0.96-0.72-0.94

Germany – Average Performance Score: -0.87

NoFamily NameGiven NameNationRacesRank
(avg)
Points
(avg)
Ski Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
4VoigtVanessaGER
44.841.0-0.71-1.04-0.69-0.74
4HerrmannDeniseGER
34.342.0-1.61-0.190.56-0.62
4DollBenediktGER
34.045.0-1.17-0.59-0.42-0.81
4NawrathPhilippGER
33.346.7-1.35-1.23-1.28-1.31

Sweden – Average Performance Score: -0.78

NoFamily NameGiven NameNationRacesRank
(avg)
Points
(avg)
Ski Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
NoFamily NameGiven NameNationRacesRank
(avg)
Points
(avg)
Ski Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
5OebergHannaSWE
32.750.3-1.260.750.20-0.47
5OebergElviraSWE
32.750.3-2.19-0.57-0.49-1.35
5PonsiluomaMartinSWE
46.038.3-1.37-0.74-0.43-0.94
5SamuelssonSebastianSWE
46.038.3-0.860.270.12-0.35

ROC – Average Performance Score: -0.74

NoFamily NameGiven NameNationRacesRank
(avg)
Points
(avg)
Ski Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
NoFamily NameGiven NameNationRacesRank
(avg)
Points
(avg)
Ski Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
6NigmatullinaUlianaRUS
34.047.3-0.63-0.370.08-0.33
6ReztsovaKristinaRUS
53.250.0-1.30-0.35-1.28-1.18
6LoginovAlexandrRUS
42.352.5-1.55-0.45-0.25-0.93
6LatypovEduardRUS
32.750.0-1.530.520.47-0.53

Italy – Average Performance Score: -0.64

NoFamily NameGiven NameNationRacesRank
(avg)
Points
(avg)
Ski Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
NoFamily NameGiven NameNationRacesRank
(avg)
Points
(avg)
Ski Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
7VittozziLisaITA
57.236.6-0.82-0.57-1.62-1.09
7WiererDorotheaITA
45.838.8-0.720.410.54-0.11
7BormoliniThomasITA
57.435.6-0.960.040.13-0.42
7HoferLukasITA
36.737.0-0.79-1.01-1.11-0.94

Ukraine – Average Performance Score: -0.53

NoFamily NameGiven NameNationRacesRank
(avg)
Points
(avg)
Ski Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
NoFamily NameGiven NameNationRacesRank
(avg)
Points
(avg)
Ski Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
8SemerenkoValentinaUKR
310.031.0-0.23-0.37-0.32-0.28
8DzhimaYuliiaUKR
48.533.5-0.740.360.13-0.28
8PrymaArtemUKR
56.836.6-0.30-0.48-0.75-0.50
8PidruchnyiDmytroUKR
56.836.6-0.91-0.96-1.29-1.06

Czech Republic – Average Performance Score: -0.43

NoFamily NameGiven NameNationRacesRank
(avg)
Points
(avg)
Ski Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
9JislovaJessicaCZE
59.033.2-0.46-0.96-0.88-0.68
9DavidovaMarketaCZE
59.033.2-1.13-0.220.50-0.40
9KarlikMikulasCZE
413.327.8-0.640.451.050.13
9KrcmarMichalCZE
411.829.3-0.80-0.59-0.78-0.77

Austria – Average Performance Score: -0.37

NoFamily NameGiven NameNationRacesRank
(avg)
Points
(avg)
Ski Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
NoFamily NameGiven NameNationRacesRank
(avg)
Points
(avg)
Ski Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
10SchwaigerJuliaAUT
412.529.3-0.16-0.280.440.06
10HauserLisa TheresaAUT
38.338.7-1.030.540.21-0.37
10EderSimonAUT
511.232.8-0.54-0.58-1.09-0.76
10LeitnerFelixAUT
413.327.8-0.59-0.42-0.15-0.40

Posted in Statistical analysis | Tagged 2022 Winter Olympics

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