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Category: Long-term trends

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 Östersund 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

2021–22 z-Scores compared to Trimester 1 of 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
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

2021–22 z-Scores compared to Trimester 1 of 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
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 2021–22 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 it’s an interesting visualization that gives a unique historical perspective that you don’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

Canadian biathletes finding balance

Posted on 2021-04-06 | by biathlonanalytics | Leave a Comment on Canadian biathletes finding balance

With three top 10s, sixteen top 20s and twenty-five top 30s in individual events, the 2020-2021 season was the best Canadian season in the last five years. After a dip in total World cup points in the 2019-2020 season, last season continued on the five-year trend of continuous increase in total world cup points:

So what did Canada do well to get here, and where is there still room for improvement? That is what the following review of the Canadian individual performances in the 2020-2021 season will analyse! In this analysis, I will only show some examples of Canadian performances, but a supportive dashboard with all used data for this analysis can be found on my Tableau Public page. It shows all stats for the Canadian team, both men and women, and has a second tab where a Canadian athlete can be selected to show his or her specific stats.

Skiing time

The skiing is still the area where the Canadian team overall can gain the most time. In the specific example below I compare the average ski times of the Canadian athletes to the field average, the average of the top 30 athletes, and the average of the top 10 athletes:

Although we can see Canada is closing up the gap a little compared to last season, we are still behind the overall average, and far behind the top 10 athletes. But the positive news is that we made great progress in the 2020-2021 season, indicated by the steep drop towards the averages.

Shooting time

This is where Canada has the least to gain (and the most to loose) as we continue to be very strong in fast shooting, leading many of the true biathlon nations:

Shooting accuracy

In previous seasons the fast shooting times by the Canadians often lead to less desired shooting percentages, but it appears the team and coaches are starting to find a balance between shooting fast and shooting accurate, demonstrated by both increased accuracy in Prone and Standing:

Shooting speed -vs- Accuracy

This balance between shooting fast and clean is of course the golden grail of shooting in biathlon (I wrote about this in more depth on this website in December last year). The chart below shows how this balance has changed for the different team, starting in 2016-2017 (thin line) and how teams have moved from shooting more or less accurate, and faster or slower. We see that in 2018-2019 team Canada put a lot of focus on shooting faster, which worked, but at the expense of less accuracy. Now we see that we have lost some speed (but are still very fast) but have gained accuracy. Hopefully, this trend can continue upwards towards more accuracy while remaining fast.

Individuals

The second part of the evaluation is looking at the individual athletes. All Canadian athletes can be viewed on the interactive dashboard, but here I’m letting Emma Lunder be the example here. We can see that here Accuracy -vs- Speed trend is similar to the Team’s and that she is gaining accuracy while giving up a little speed. But we can also see that here range, shooting and prep time (prep = range – shooting) is still very good and well below the field’s average:

Her total percentage of shooting accuracy (T) has increased mostly based on a strong increase in her prone shooting (P) and she well above the field’s average Total shooting percentage (T avg.). Lastly her skiing speed seems to have stabilized and is on par with the field’s average. But she is also closing the gap with the Top 30 and Top 10 averages.

Conclusion

As mentioned team Canada had a great season, as did Emma Lunder. The above will hopefully give a good explanation of how to read the interactive dashboard to see both men’s and women’s team performances for individual races, and the individual dashboards for any of the Canadian athletes. If the current trends continue and with some of the younger athletes coming through the pipeline, hopefully, we can some Canadian podiums in the next season! Go Canada!

Posted in Biathlon Media, Long-term trends, Statistical analysis

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