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Year: 2022

Peak age for biathletes at the World Cup level

Posted on 2022-05-07 | by biathlonanalytics

Introduction

A couple of weeks ago, just as I was finishing up my analysis on the strength of biathlon nations, there was an article* on FasterSkier about Trends in Age and Ski Performance, for cross country skiers on the FIS World Cup. Curious about what this would look like for biathlon, I did some data digging to get race results as well as athlete birthdays to calculate their ages on race day. And then I got distracted and forgot about it. Luckily Matthias Ahrends, a super friendly biathlon coach from Canmore sent me an email about the article and asked if that was something I could look into for biathlon. Yes, I can! And I did.

Trends in Age and Ski Performance: A Second Look by Ella DeWolf and Andrew Siegel

* The original article, Analysis: Performance and Age, was written by Joran Elias, also known as StatisticalSkier

Data

The data for all the used non-team race results (World Cup level, including Olympic Games) and biathletes is from the 2009-2010 season up to and including the 2021-2022 season. This includes 1,102 athletes, 658 races, 47,458 race participants and 5,129 age-athlete combinations varying from 16 to 48 years old. Unfortunately, two athletes’ birthdays are incorrect in the data source (Romana Schrempf and Andreea Mezdrea) leading to incorrect age calculations, so they have been excluded.

The ages of athletes were calculated at the race level, based on their birthdays and the race date.

The biathlon performance is based on points that were calculated for all races, including Olympic ones, based on race rankings according to the current IBU rule book. For pursuit races, I calculated points based on the isolated race results (ignoring start time differences) as that gives a better indicator of performance.

Recent retroactive disqualifications by the IBU excluded the involved athletes from the rankings and moved up all lower-ranked athletes one position in the rankings.

Total points per age per athlete

Following the article, we first look at the total (calculated) points per athlete, one age at a time, which shows at what age athletes score the most points. The ages are shown from left to right, and the points from bottom to top.

We can see in the chart that women scored the highest number of points in the ages just after turning 30. One thing to consider is that typically the more successful athletes may be able to continue their successes a little longer, which could explain why the peak in total points is rather late. As we can see from the confidence band, the confidence of the trendline is lower as we get to higher ages, as it is based on fewer athletes.

The male athletes show a similar pattern, with the peak of most points around the age of 33. These are just the total number of points scored at a certain age for every athlete, not considering the total number of athletes and races in that age group. In case you are wondering about the single, fairly high line on the right of the chart? I call it the OEB effect. Ole Einar Bjørndalen affected the trendline quite substantially, specifically at higher ages.

Average points per athlete per age

The next chart shows the same data as above but averages the points per age per athlete. The darker areas indicate overlapping athlete-age combinations. The peak of the average scores happens around the early 30s again, with slightly higher averages by the women compared to the men. This is possibly caused by the larger number of male athletes, as shown a few charts down.

Number of seasons

Still following the structure of the mentioned cross country skiers article, I looked at the number of seasons the athletes participated in. When looking at this chart, please keep in mind that although it is still useful, it provides an incomplete picture. Although the data starts in the 2009-2010 season, not all athletes represented in the data started their first year in that season. The athletes that were active before the 2009-2010 season will be shown as if they started their first season in 2009-2010. For example, Ole Einar Bjørndalen raced from 1993 until 2018, an incredible 26 seasons. But he will show up in this chart as an athlete with nine seasons (2009-2010 until 2017-2018).

We can see that a large proportion of the dataset only races in a few seasons. The ones that race for ten seasons or more represent about 8.5% of the total dataset.

Stats overview

When combining all points from all athletes per age, we see that women score the most points at age 26, and men at 28.

The highest number of athletes peaks a bit earlier, at age 23 and 24 for women and men.

The average points per athlete per age follow a fairly smooth pattern until age 30, after which the impact of some major athletes disrupts it. Women athletes like Kaisa Makarainen, Andrea Henkel, Olga Zaitseva and Anastasiya Kuzmina still scored a lot of points at age 34, and Ole Einar Bjørndalen still produced 730 points at age 41, and 431 at age 43! Those point totals combined with very few athletes still racing at those ages leads to high point averages per athlete per age.

When we look at the number of races athletes participated in per age, we see a plateau of about 320 races up to age 32, 33 after which they start dropping quite quickly. The sudden uptake amongst the women at age 37 is thanks to athletes like Magdalena Gwizdon, Anna Carin Zidek, Andrea Henkel, Susan Dunklee and Selina Gasparin. Some of the most active men in their 40s are Ole Einar Bjørndalen, Ilmars Bricis, Daniel Mesotitsch, Halvard Hanevold and Oystein Slettemark.

The number of participants per age peaks around 25 and levels off pretty fast after that. When dividing the total points per age by the number of race participants we see quite similar trends to the average-points-per-athlete chart. Ole Einar Bjørndalen and Halvard Hanevold are mostly responsible for the high levels at 40 and up.

Concluding

I think the analysis above confirms the general assumption that biathletes typically perform at their strongest between the early and mid-30s, with some exceptional male athletes still performing at high levels in their 40s. Although that, with all respect to the other athletes, can be mostly contributed to the OEB effect.

Did you like this article, or do you have questions or comments? Please reach out on Twitter!

Posted in Biathlon News, Statistical analysis

Most improved athletes of last season

Posted on 2022-03-28 | by real biathlon | Leave a Comment on Most improved athletes of last season

Improvements in Total Performance Scores of regular World Cup athletes season-to-season. The last row of both tables shows changes in overall scores for the 2021–22 season compared to performances one season earlier (only athletes who appeared in at least half the races each season). 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

2021–22 z-Scores compared to 2020–21 | Non-Team events

Winning his first top 10 result this season, American Paul Schommer was the most improved male athlete, with career bests both in terms of shooting accuracy and ski speed. Vytautas Strolia also managed his first career top 10 this winter, coming second on this list, mostly thanks to skiing almost 2% faster than last year. In contrast, Martin Ponsiluoma and Sturla Holm Lægreid both underperformed compared to 2020–21, even though Lægreid managed to finish the season strong and repeated his 2nd place in the overall standings.

Quentin Fillon Maillet only improved marginally over last winter (1.3% better hit rate, 0.5% faster skiing), but it was more than enough to win his first Overall World Cup title comfortably. Johannes Thingnes Bø had by far the worst shooting stats of his career (82.1% hit rate, a whole 10% lower than only two seasons ago), however, he was still the field’s fastest skier and he delivered when it counted most in Beijing, winning 4 Olympic gold medals.

2021–22 z-Scores compared to 2020–21 | 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
NoFamily NameGiven NameNationRacesSki Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
Change
1SchommerPaulUSA
18-0.18-0.68-0.35-0.35-0.45
2StroliaVytautasLTU
23-0.67-0.51-0.15-0.56-0.35
3DudchenkoAntonUKR
14-0.65-0.810.59-0.55-0.27
4SmolskiAntonBLR
19-1.19-0.690.20-0.88-0.22
5FemlingPeppeSWE
14-0.44-0.81-0.95-0.61-0.21
6LatypovEduardRUS
14-1.34-0.62-0.28-1.01-0.21
7SeppalaTeroFIN
25-1.03-0.50-0.45-0.80-0.19
8KuehnJohannesGER
21-1.210.02-0.03-0.71-0.18
9ChristiansenVetle S.NOR
24-1.24-1.24-0.52-1.15-0.17
10KobonokiTsukasaJPN
20-0.09-1.04-0.11-0.37-0.14
11LesserErikGER
20-1.01-1.21-1.44-1.12-0.14
12ReesRomanGER
25-0.75-1.25-0.17-0.82-0.11
13Fillon MailletQuentinFRA
26-1.55-1.19-1.01-1.38-0.09
14BormoliniThomasITA
25-0.58-0.66-0.60-0.60-0.09
15BrownJakeUSA
19-0.80-0.000.71-0.39-0.08
16ClaudeFabienFRA
24-1.17-0.10-1.00-0.84-0.07
17LangerThierryBEL
14-0.32-0.340.26-0.25-0.06
18GowScottCAN
16-0.45-0.23-1.10-0.46-0.05
19StvrteckyJakubCZE
18-0.830.680.84-0.19-0.05
20DollBenediktGER
25-1.28-0.63-0.45-0.99-0.04
21WegerBenjaminSUI
18-0.76-1.24-0.32-0.85-0.03
22LeitnerFelixAUT
23-0.67-0.80-0.31-0.66-0.02
23GuigonnatAntoninFRA
21-0.90-0.39-0.92-0.75-0.01
24SamuelssonSebastianSWE
24-1.44-0.62-0.54-1.09+0.02
25DesthieuxSimonFRA
26-1.18-0.81-0.51-0.99+0.02
26GowChristianCAN
18-0.18-1.24-1.00-0.58+0.03
27DovzanMihaSLO
15-0.06-0.78-1.16-0.40+0.06
28LoginovAlexandrRUS
18-1.41-0.26-0.14-0.92+0.08
29PidruchnyiDmytroUKR
14-0.91-0.07-0.20-0.58+0.09
30BoeTarjeiNOR
22-1.31-0.92-0.28-1.07+0.09
31BionazDidierITA
14-0.33-0.190.81-0.16+0.09
32ZahknaReneEST
150.34-0.720.02-0.00+0.10
33NelinJesperSWE
17-0.900.370.38-0.38+0.11
34JacquelinEmilienFRA
25-1.28-0.44-1.04-1.01+0.11
35IlievVladimirBUL
18-0.880.590.48-0.29+0.13
36KrcmarMichalCZE
25-0.79-0.660.01-0.65+0.14
37ClaudeFlorentBEL
20-0.24-0.530.24-0.27+0.14
38EderSimonAUT
25-0.57-1.31-1.19-0.86+0.14
39BoeJohannes T.NOR
17-1.78-0.40-0.28-1.20+0.21
40LaegreidSturla HolmNOR
23-1.35-0.90-0.90-1.17+0.22
41PonsiluomaMartinSWE
23-1.390.59-0.86-0.75+0.22
42DohertySeanUSA
21-0.430.26-0.36-0.22+0.24
43WindischDominikITA
18-0.770.490.00-0.31+0.24
44MukhinAlexandrKAZ
15-0.110.740.980.26+0.24
45HoferLukasITA
24-0.82-0.96-0.30-0.80+0.28
46GuzikGrzegorzPOL
140.160.910.540.42+0.30
47SimaMichalSVK
150.190.120.210.17+0.30
48KomatzDavidAUT
18-0.05-0.800.51-0.20+0.34
49SinapovAntonBUL
130.011.040.960.42+0.57

Women

Jessica Jislová was the most improved athlete on the women’s side, skiing roughly 1% faster than last season and raising her non-team hit rate by 13.9% (among regular World Cup athletes, she was the 4th-most accurate overall). She is followed by Deedra Irwin, who managed the United States’ best ever non-team result in Olympic history, and Sweden’s Anna Magnusson, who got her stats almost back to her 2016–17 level, her career best season.

While Marte Olsbu Røiseland did improve over last winter (-0.2%), her performance uptick wasn’t as extreme as you might expect. Last year’s World Cup winner Tiril Eckhoff was worse, but according to this metric only marginally (+0.1%); in fact, her hit rate didn’t change much at all (-2.1%). Clearly, it’s sometimes more important when you miss your shots, not so much how you average out over a season.

2021–22 z-Scores compared to 2020–21 | 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
NoFamily NameGiven NameNationRacesSki Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
Change
1JislovaJessicaCZE
25-0.59-1.11-0.49-0.73-0.62
2IrwinDeedraUSA
19-0.33-0.510.32-0.31-0.54
3MagnussonAnnaSWE
16-0.84-0.40-0.51-0.67-0.52
4LieLotteBEL
22-0.39-1.03-0.87-0.63-0.44
5OebergElviraSWE
24-1.79-0.51-1.06-1.33-0.42
6SolaHannaBLR
18-1.620.28-1.10-1.01-0.41
7BrorssonMonaSWE
21-1.00-0.70-0.83-0.89-0.40
8FialkovaIvonaSVK
19-1.060.66-0.26-0.47-0.39
9ChevalierChloeFRA
20-1.14-0.17-0.28-0.76-0.34
10MinkkinenSuviFIN
17-0.10-1.21-0.78-0.50-0.27
11Braisaz-BouchetJustineFRA
25-1.840.42-0.50-1.02-0.26
12TodorovaMilenaBUL
18-1.020.19-0.00-0.55-0.26
13Chevalier-BouchetAnaisFRA
24-1.22-0.51-1.48-1.05-0.26
14RoeiselandMarte OlsbuNOR
24-1.66-1.09-1.34-1.45-0.20
15AlimbekavaDzinaraBLR
19-1.39-0.77-0.49-1.10-0.19
16TomingasTuuliEST
18-0.66-0.160.45-0.39-0.18
17SimonJuliaFRA
25-1.27-0.13-1.74-0.99-0.17
18TachizakiFuyukoJPN
18-0.26-0.650.20-0.32-0.15
19KlemencicPolonaSLO
17-0.200.48-0.060.02-0.15
20BescondAnaisFRA
25-1.25-0.07-0.20-0.78-0.15
21MaedaSariJPN
15-0.760.960.43-0.12-0.12
22OjaReginaEST
16-0.090.21-0.60-0.06-0.08
23NigmatullinaUlianaRUS
18-1.01-0.50-0.14-0.76-0.08
24TandrevoldIngrid L.NOR
23-1.31-0.70-0.18-1.00-0.08
25HerrmannDeniseGER
23-1.55-0.34-0.18-1.04-0.08
26EderMariFIN
24-1.300.520.51-0.56-0.07
27OebergHannaSWE
24-1.560.03-1.79-1.13-0.05
28HettichJaninaGER
16-0.88-0.44-0.82-0.75-0.05
29GasparinAitaSUI
13-0.39-0.58-1.09-0.53-0.05
30EganClareUSA
18-0.63-0.350.11-0.46-0.05
31GasparinElisaSUI
13-0.48-0.20-1.00-0.46-0.04
32DavidovaMarketaCZE
25-1.41-0.46-0.20-0.99-0.03
33PerssonLinnSWE
22-1.23-0.30-0.57-0.89-0.03
34MironovaSvetlanaRUS
15-1.07-0.19-0.21-0.72-0.02
35KazakevichIrinaRUS
18-1.020.170.49-0.49-0.00
36HauserLisa TheresaAUT
26-1.10-0.81-1.53-1.06+0.00
37CharvatovaLucieCZE
20-0.930.77-0.23-0.36+0.01
38VittozziLisaITA
20-1.050.96-1.40-0.51+0.04
39HaeckiLenaSUI
22-0.88-0.09-1.24-0.69+0.04
40AvvakumovaEkaterinaKOR
13-0.34-0.150.86-0.14+0.05
41ReidJoanneUSA
16-0.520.420.12-0.17+0.09
42PreussFranziskaGER
17-1.35-0.58-0.76-1.06+0.09
43KruchinkinaElenaBLR
13-0.700.190.26-0.33+0.09
44ZukKamilaPOL
14-0.790.680.42-0.22+0.10
45KnottenKaroline O.NOR
18-0.41-0.60-1.80-0.63+0.10
46LeshchankaIrynaBLR
14-0.79-0.070.77-0.40+0.10
47EckhoffTirilNOR
21-1.70-0.22-0.77-1.16+0.11
48HinzVanessaGER
21-0.80-0.53-0.02-0.63+0.11
49PuskarcikovaEvaCZE
160.00-0.31-0.65-0.17+0.12
50WiererDorotheaITA
25-1.08-0.46-1.46-0.95+0.15
51Hojnisz-StaregaMonikaPOL
19-0.74-0.58-0.09-0.61+0.19
52ZdoucDunjaAUT
13-0.04-0.95-1.27-0.45+0.20
53BendikaBaibaLAT
19-0.820.33-0.39-0.44+0.22
54LienIdaNOR
19-1.240.710.22-0.50+0.24
55LunderEmmaCAN
17-0.26-0.21-1.46-0.39+0.29
56DzhimaYuliiaUKR
19-0.990.14-0.10-0.55+0.30
57DunkleeSusanUSA
150.03-0.020.290.04+0.38
58SchwaigerJuliaAUT
14-0.37-0.280.60-0.23+0.38
59GasparinSelinaSUI
15-0.690.73-0.07-0.20+0.48
60TalihaermJohannaEST
140.29-0.240.540.17+0.49
Posted in 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

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