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Category: Long-term trends

Looking back at Oestersund through data

Posted on 2021-12-06 | by biathlonanalytics | Leave a Comment on Looking back at Oestersund through data


With Oestersund coming to a close, it is time to review the Women’s individual performances and look at some athletes that stood out by their shooting and skiing performances based on the race data. It was nice to see that the wind that can be so typical for races in Oestersund mostly stayed away, giving us some tremendous races with fair conditions for all athletes!

The data and metrics

Based on IBU’s race data, the shooting charts shown in this article are simply expressed in the total shooting percentage (total misses divided by total shots taken). The skiing shown below uses the course time data and compares each athlete’s time with the fastest course time of the particular race. This gives us a “% behind the fastest skier” and gives us an idea of what an athlete must do to become the fastest skier of the pack.

Lisa Theresa Hauser

It is hard not to start with the current leader of the World Cup, and Hauser had some impressive races in Oestersund. And that with a ski speed that was slower (relative to the fastest skier) than her average based on the current and last season. Her shooting was top-notch and even above her already strong average of 86%. The chart below demonstrates these statistics per race event with the 2020-2021 season shown in green and the current 2021-2022 season shown in purple.

When we compare Hauser’s world cup points after four races to her previous two seasons’ four races, we can see that she is on a trend that will improve her final standing significantly if she can keep up this pace. It’s a bit early to say after only four races, but in any case, she is off to a great start of her season!

Anais Chevalier-Bouchet

Although Chevalier-Bouchet had a similarly strong start in the past season, she surprised me somewhat with her strong performance in Oestersund. Perhaps this was based on her decline at the end of last season. Her first race seemed to continue that trend, but after that first race she had some great results, especially in the shooting range. Her skiing was around her average for the current and previous season, so if she can improve there I think she’ll be someone to keep an eye on this season.

Denise Herrmann

It is an understatement that Herrmann’s previous season was disappointing, but based on her races in Oestersund Herrmann seems to be on her way back. With excellent shooting for her and improving ski speed, can she regain her form with which she won the 2019-2020 world cup sprint globe? With her ski speed still being under her average of the previous and current season, an improvement can be expected there. And if she then can keep shooting the way she was in Oestersund, contention for top spots are definitely within reach for Herrmann.

Dzinara Alimbekava

Like with Hauser, Alimbekava is a safe bet with her third position in the current world cup rankings. But I was really impressed with her consistency at a high level during her races in Oestersund. And when looking at her world cup points for the current season and comparing them to last season, this shouldn’t be a surprise. If she can avoid fading away a little like last season she could be a contender for the overall title. As she is turning 26 in January the blue bib is no longer something she can battle for, but I’m sure she would pick the yellow bib over the blue if she has the chance.

Elvira Oeberg

Lastly, the younger Oeberg sister was so impressive on her skis that I feel I cannot leave her out. Although her shooting was below her average from the current and last season, she made up many positions with her ski speed, being the fastest skier in three of the four races.

And currently in fourth place in the world cup rankings, she is right on pace with her season start last year. If she can improve on her shooting while keeping the speed it will be hard to imagine her out of the top five for upcoming races. Of course, this was a home event for Oeberg. And although that may come with some additional pressure, knowing the course really well and having a wax team that knows the conditions like no other team was a benefit she will no longer have in the rest of the season.

This concludes the women’s review of Oestersund. Part of this article was used in the IBU Biathlon Insider #2. If you are not yet subscribed to that, go to biathlonworld.com and scroll to the bottom then hit the Subscribe button! Or see the visuals “live” on my Tableau Public page. Or check my previous posts.

Posted in Long-term trends, Statistical analysis | Tagged Biathlon Analytics, Biathlon Insider

Improvement season-to-season

Posted on 2021-11-28 | by real biathlon | Leave a Comment on Improvement season-to-season

Changes in the Total Performance Scores of regular World Cup athletes. The tables show improvement and decline in z-scores from this weekend’s Season Opening in ร–stersund compared to trimester 1 of last season (roughly December 2020, only athletes with at least 3 races in last winter’s trimester 1). 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

2021โ€“22 z-Scores compared to Trimester 1 of 2020โ€“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
1TodevBlagoyBUL
20.84-0.53-0.940.23-1.06
2GowScottCAN
2-0.25-1.56-1.74-0.81-1.00
3DudchenkoAntonUKR
2-0.76-0.53-1.04-0.73-0.72
4BrownJakeUSA
2-1.05-0.531.02-0.65-0.54
5SchommerPaulUSA
2-0.11-1.21-0.15-0.44-0.50
6DesthieuxSimonFRA
2-1.53-1.21-0.72-1.34-0.49
7StvrteckyJakubCZE
2-0.78-0.180.95-0.40-0.44
8KobonokiTsukasaJPN
20.24-1.210.33-0.17-0.41
9DovzanMihaSLO
20.17-1.21-1.40-0.42-0.40
10ZahknaReneEST
20.45-0.870.430.07-0.37
11StroliaVytautasLTU
2-0.90-0.531.30-0.53-0.36
12TsymbalBogdanUKR
20.11-0.53-1.50-0.27-0.31
13StefanssonMalteSWE
2-0.211.190.860.32-0.29
14ClaudeFabienFRA
2-1.73-0.18-1.50-1.26-0.28
15GowChristianCAN
2-0.47-1.21-1.33-0.79-0.27
16LeitnerFelixAUT
2-1.08-0.18-0.55-0.76-0.27
17PidruchnyiDmytroUKR
2-0.54-0.87-0.82-0.67-0.24
18StalderSebastianSUI
2-0.04-0.18-0.97-0.19-0.22
19MagazeevPavelMDA
20.24-0.53-1.34-0.17-0.21
20LatypovEduardRUS
2-1.00-1.21-0.01-0.95-0.15
21SiimerKristoEST
21.030.500.310.79-0.10
22DombrovskiKarolLTU
2-0.35-0.180.21-0.23-0.09
23ClaudeFlorentBEL
2-0.22-0.530.28-0.25-0.08
24NelinJesperSWE
2-1.32-0.180.52-0.77-0.04
25FemlingPeppeSWE
2-0.470.16-1.29-0.38-0.02
26SmolskiAntonBLR
2-1.28-0.180.23-0.78-0.02
27LazouskiDzmitryBLR
2-0.20-0.181.04-0.05+0.01
28HornPhilippGER
2-0.860.16-0.90-0.57+0.05
29ChristiansenVetle SjaastadNOR
2-1.50-0.870.64-1.06+0.10
30BoeTarjeiNOR
2-1.38-0.87-0.59-1.14+0.10
31BormoliniThomasITA
2-0.31-0.53-0.93-0.45+0.10
32BauerKlemenSLO
2-0.040.50-1.09-0.01+0.11
33SzczurekLukaszPOL
21.131.190.551.08+0.11
34GuigonnatAntoninFRA
2-1.210.16-0.39-0.71+0.11
35HiidensaloOlliFIN
2-0.210.160.800.02+0.12
36BanysLinasLTU
20.950.50-0.410.66+0.12
37EderSimonAUT
2-0.41-1.21-1.55-0.78+0.15
38SlotinsRobertsLAT
20.420.851.490.67+0.15
39SamuelssonSebastianSWE
2-2.000.160.44-1.08+0.15
40ReesRomanGER
2-0.900.16-0.15-0.50+0.18
41MukhinAlexandrKAZ
20.280.850.450.46+0.18
42VarabeiMaksimBLR
2-0.951.191.39-0.05+0.20
43BoeJohannes ThingnesNOR
2-1.61-1.210.02-1.30+0.20
44IlievVladimirBUL
2-0.460.500.38-0.08+0.22
45JacquelinEmilienFRA
2-1.990.50-0.63-1.10+0.23
46OzakiKosukeJPN
2-0.180.851.970.38+0.26
47BalogaMatejSVK
21.350.50-0.140.92+0.27
48TrsanRokSLO
21.20-1.56-0.680.18+0.27
49WindischDominikITA
2-0.421.19-1.09-0.03+0.28
50LoginovAlexanderRUS
2-1.210.16-0.48-0.73+0.29
51LangerThierryBEL
2-0.100.50-0.080.08+0.30
52RasticDamirSRB
20.660.503.220.92+0.33
53BocharnikovSergeyBLR
2-0.820.85-0.79-0.33+0.34
54LaegreidSturla HolmNOR
2-0.71-1.56-0.91-0.98+0.34
55Fillon MailletQuentinFRA
2-1.36-0.18-0.71-0.94+0.39
56DollBenediktGER
2-0.98-0.18-0.28-0.67+0.39
57KrcmarMichalCZE
2-0.740.160.08-0.38+0.40
58FakJakovSLO
2-0.37-1.21-0.85-0.67+0.42
59HarjulaTuomasFIN
20.620.16-1.650.21+0.43
60YaliotnauRamanBLR
2-0.511.531.180.28+0.43
61WegerBenjaminSUI
2-0.37-0.870.17-0.45+0.43
62KomatzDavidAUT
20.16-0.530.540.01+0.45
63GuzikGrzegorzPOL
20.141.190.710.51+0.46
64BionazDidierITA
20.11-0.180.610.09+0.47
65DohertySeanUSA
20.020.85-0.240.23+0.48
66SimaMichalSVK
20.550.850.080.58+0.54
67DaleJohannesNOR
2-1.240.160.88-0.58+0.64
68TachizakiMikitoJPN
21.03-0.870.680.44+0.65
69EberhardJulianAUT
20.060.16-0.270.05+0.72
70SinapovAntonBUL
20.421.530.760.78+0.72
71EliseevMatveyRUS
20.71-1.21-0.260.04+0.85
72VaclavikAdamCZE
20.031.881.190.70+0.88
73PonsiluomaMartinSWE
2-1.532.56-1.12-0.29+0.91
74GerdzhikovDimitarBUL
20.452.560.151.03+0.99
75HoferLukasITA
20.14-0.530.930.04+1.00
76GaranichevEvgeniyRUS
20.590.85-0.800.50+1.21

Women

2021โ€“22 z-Scores compared to Trimester 1 of 2020โ€“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
1BendikaBaibaLAT
2-0.74-0.87-0.34-0.73-0.83
2LieLotteBEL
2-0.31-1.28-0.67-0.63-0.61
3SolaHannaBLR
2-1.29-0.05-1.47-0.95-0.55
4NigmatullinaUlianaRUS
2-0.60-1.68-0.33-0.88-0.43
5MagnussonAnnaSWE
2-0.72-0.46-0.13-0.57-0.37
6DavidovaMarketaCZE
2-1.24-1.68-0.15-1.24-0.37
7KliminaDaryaKAZ
20.16-0.052.040.33-0.35
8JislovaJessicaCZE
2-0.37-0.46-0.51-0.41-0.30
9FialkovaIvonaSVK
2-0.481.180.350.10-0.25
10AlimbekavaDzinaraBLR
2-1.46-1.28-0.44-1.29-0.23
11WeidelAnnaGER
2-0.09-2.09-0.74-0.75-0.23
12AvvakumovaEkaterinaKOR
20.27-0.46-0.060.02-0.22
13LescinskaiteGabrieleLTU
20.88-1.680.760.12-0.21
14CharvatovaLucieCZE
2-0.880.77-1.04-0.42-0.19
15BrorssonMonaSWE
2-1.09-0.46-0.84-0.88-0.18
16BescondAnaisFRA
2-1.32-0.05-0.29-0.83-0.17
17PuskarcikovaEvaCZE
2-0.44-0.46-0.11-0.40-0.16
18VittozziLisaITA
2-0.920.36-1.99-0.68-0.15
19EderMariFIN
2-1.05-0.051.45-0.46-0.14
20HauserLisa TheresaAUT
2-0.95-0.87-1.97-1.05-0.14
21KondratyevaAnastassiyaKAZ
21.80-0.873.001.17-0.13
22Chevalier-BouchetAnaisFRA
2-1.21-0.05-1.16-0.87-0.10
23LienIdaNOR
2-0.92-0.051.01-0.43-0.07
24IrwinDeedraUSA
20.300.360.190.31-0.05
25Braisaz-BouchetJustineFRA
2-1.650.36-0.40-0.91-0.05
26TodorovaMilenaBUL
2-0.650.77-0.26-0.19-0.03
27KalkenbergEmilie AagheimNOR
2-0.25-0.05-0.71-0.25-0.01
28OebergElviraSWE
2-2.150.77-0.91-1.15+0.00
29DzhimaYuliiaUKR
2-0.74-0.05-0.23-0.48+0.03
30RoeiselandMarte OlsbuNOR
2-1.63-0.46-1.32-1.25+0.05
31HerrmannDeniseGER
2-1.23-0.870.41-0.93+0.06
32PreussFranziskaGER
2-1.51-0.05-0.83-1.00+0.06
33KruchinkinaElenaBLR
2-0.35-0.05-0.35-0.26+0.07
34MironovaSvetlanaRUS
2-0.820.36-0.44-0.43+0.11
35KlemencicPolonaSLO
20.021.180.130.37+0.11
36TachizakiFuyukoJPN
20.06-0.870.93-0.11+0.12
37ChevalierChloeFRA
2-1.021.18-0.30-0.29+0.12
38LunderEmmaCAN
2-0.04-1.28-2.13-0.65+0.16
39EganClareUSA
2-0.51-0.460.51-0.37+0.17
40TomingasTuuliEST
2-0.31-0.46-0.01-0.32+0.18
41KuklinaLarisaRUS
2-0.58-0.05-0.61-0.43+0.19
42GasparinSelinaSUI
2-0.20-0.05-0.59-0.20+0.20
43PerssonLinnSWE
2-1.210.77-0.99-0.61+0.21
44HinzVanessaGER
2-0.31-0.46-0.13-0.33+0.21
45KocerginaNataljaLTU
20.410.361.160.49+0.24
46HettichJaninaGER
2-0.51-0.05-0.61-0.39+0.25
47LehtlaKadriEST
21.76-1.28-0.410.62+0.32
48TandrevoldIngrid LandmarkNOR
2-1.19-0.050.46-0.66+0.33
49EckhoffTirilNOR
2-1.631.18-0.82-0.71+0.34
50OebergHannaSWE
2-1.670.77-1.40-0.93+0.34
51Hojnisz-StaregaMonikaPOL
2-0.330.36-0.20-0.11+0.35
52KadevaDanielaBUL
20.610.77-0.060.58+0.38
53JankaErikaFIN
21.500.36-0.190.97+0.38
54BeaudrySarahCAN
21.26-1.28-0.900.27+0.41
55HaeckiLenaSUI
2-0.230.36-1.25-0.18+0.41
56KnottenKaroline OffigstadNOR
20.23-1.68-1.25-0.50+0.42
57MaedaSariJPN
2-1.013.23-0.570.27+0.42
58ZdoucDunjaAUT
20.51-1.28-0.40-0.11+0.42
59GasparinElisaSUI
2-0.120.36-0.120.02+0.49
60GasparinAitaSUI
20.040.36-0.800.03+0.49
61SchwaigerJuliaAUT
20.07-0.460.32-0.05+0.49
62ZukKamilaPOL
2-0.331.590.750.36+0.58
63SimonJuliaFRA
2-1.121.59-1.38-0.36+0.58
64WiererDorotheaITA
2-1.16-0.051.65-0.50+0.59
65MinkkinenSuviFIN
20.51-0.050.550.36+0.60
66HorvatovaHenrietaSVK
22.24-0.46-0.111.18+0.61
67RiederChristinaAUT
20.61-0.460.310.27+0.61
68SemerenkoValentinaUKR
20.14-0.05-0.86-0.03+0.62
69BekhEkaterinaUKR
20.391.59-0.950.58+0.66
70LeshchankaIrynaBLR
2-0.110.360.560.11+0.79
71ReidJoanneUSA
2-0.151.591.370.53+0.81
72TalihaermJohannaEST
20.881.181.121.00+0.83
73HachisukaAsukaJPN
21.711.591.891.70+1.08
74SabuleAnnijaLAT
22.910.77-0.011.94+1.20
75KazakevichIrinaRUS
2-0.693.640.530.71+1.31
Posted in Long-term trends, Statistical analysis | Tagged 2021โ€“22 season, shooting, skiing

Biathlon World Cup wins | Men (1958 – 2021)

Posted on 2021-09-10 | by real biathlon | Leave a Comment on Biathlon World Cup wins | Men (1958 – 2021)

I have been experimenting with bar chart races lately. I think itโ€™s an interesting visualization that gives a unique historical perspective that you donโ€™t get by simply looking up records.

Here is one of my first attempts, non-team victories in men’s World Cup level races (Biathlon World Cup, World Championships, Olympics) from 1958- 2021. I hope you like it.

Women:

Posted in Biathlon Media, Long-term trends

Why the French weren’t more competitive

Posted on 2021-06-26 | by biathlonanalytics | Leave a Comment on Why the French weren’t more competitive

Introduction

After the 2019-2020 season, Martin Fourcade retired from biathlon, leaving a strong group of athletes on the French men’s team. They appeared ready to challenge the Norwegians, with Q Fillion Maillet and E Jacquelin the main contenders already having a good season and getting close in the 2019-2020 season. However, the 2020-2021 season turned out a little different, with still strong performances from all French team members, but it quickly felt they were not going to challenge for the yellow bib.

This analysis tries to find out where things stagnated for the French and prevented QFM and EJ from at least challenging for the crystal globe(s).

World Cup Points

First, I wanted to see how the World Cup Points compared to the eventual yellow bib winner, per season (which happened to be this fella called JT Boe all three seasons!). The charts below show Johannes Tingnes Boe in red, Quentin Fillon Maillet in dark blue and Emilien Jacquelin in light blue:

We can see that after closing the gap with JT Boe in the 2019-2020 season, it has widened again on the 2020-2021 season, mostly for QFM but to some degree for EJ as well. Now let’s look at the discipline level:

The Sprint and Pursuit events show the same story, being very close to Boe in 2019-2020 but losing ground in 2020-2021, especially in the sprint. In the Sprint, EJ is behind QFM in every season, but for the Pursuit EJ first follows, then equals and eventually surpasses QFM in the last season.

The Individual and Mass Starts were the events where most progress were made, as QFM actually does better than Boe in the last season and is very close in all three seasons in the Mass Start. EJ drops in the final season compared to the previous one.

Ski speed

Now let’s look at more detail, starting with ski speed compared to the field’s average. Note that the vertical axis is reversed, as -4 means 4% below the field average, which is a good thing, so it should be at the top.

It’s no surprise that JT Boe is the fastest of the three but where in 2019-2020 the Frenchmen were seemingly getting closer, the gap widened again in last season:

This was due to JT Boe being even faster, but both Frenchmen losing speed compared to the field average. They are still quite a bit faster than the field average, but clearly need to rely on shooting time and quality if they want to catch up. Let’s look at that next.

Range Time

Looking at the time on the range we can see this is where the French athletes gain time on Boe, not in the least due to Boe getting slower, but QFM and EJ are also getting faster from one season to the next. We noticed Boe having some shooting struggles, and I also clearly remember the super fast shooting by EJ at the World Championships. He also proved later in the season the fast shooting doesn’t always work out, but generally I would say the French are stronger here.

Of course a fast range time only works well when you hit most targets, so that the next thing we’ll look at.

Shooting Time and Percentage

The chart below shows combined Shooting Percentage from left to right and Shooting Time compared to Field Average from bottom to top. It then plots the three seasons per athlete to show the change and development from season to season. Generally EJ is in the top left corner, meaning he shoots fast but not most accurately. QFM has started shooting faster but lost some shooting quality. Boe is clearly the slower shooter of the three, and although his percentage was up in the 2019-2020 season, last season he dropped back to around 85%, the lowest of the three.

Conclusions

Since Boe is still by far the fastest skier, the Frenchmen needed to keep the distance in ski speed as small as possible, which they failed to do in the last season as the gap widened. They do perform better in the shooting range though and if Boe continues to struggle this would be an area where the Frenchmen can gain the most ground. Even if the existing difference in shooting speed and quality stays the same as last season, closing the gap a little on the skis will allow them to be more competitive again for the upcoming season. According to the guys at Sport in Depth (I wrote about them in my previous article), they will still end up behind Boe (and Laegreid). I can’t wait for the new season to get started so we can actually find out for ourselves!

Posted in Long-term trends, Statistical analysis | Tagged French, performance, review

Predicting race results

Posted on 2021-06-18 | by biathlonanalytics | Leave a Comment on Predicting race results

In February I wrote about a Tableau report that didn’t necessarily predict race outcomes for the World Championships but helped inform people to make better predictions or guesses. I still believe it helps to know who’s hot and not (recent race results), previous race results at the venue and discipline, and current standing in the World Cup, but I ran into a website that has built a model to predict race and season outcomes, and their results are interesting, to say the least.

When you consider how unpredictable biathlon can be, any decent results from a model are impressive if you ask me. In this article they describe how their model did against reality, and the results were good in many cases. In addition they have more articles evaluating their performance, and I would agree that they did well.

But models remain exactly that, models. And I am sure if the IBU held 150,000 races the reality would be closer to the models. But that’s the beauty of sport and biathlon: we don’t run the races that many time, so we have to consider the form of the day, athletes feeling sick but still participating, harder and softer snow conditions during the race, a loud crowd at the shooting range (remember those days?!?), etc. we can go and on about external qualitative factors that can, and will, impact race results. If a model predicts Laegreid to challenge for the yellow bib, and Hauser to win a specific race, I think they are on to something worth checking out.

I’m no betting person so I don’t see myself using it for that purpose ever, although I did participate in a fun fantasy game that relies on race predictions to determine the “most knowledgable biathlon person”. But in line with the dashboard I created myself, I think the model does a good job providing additional information that can help inform predictions, and also give more insight into biathlon and its athletes. And in the end, I’m happy it doesn’t predict race results with 95% certainty, as that would quickly end my interest in biathlon altogether!

Posted in Long-term trends, Statistical analysis

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