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

Recent Articles

  • Norwegian Dominance
  • Overall performance scores, season-to-season improvements
  • “Whether the weather is better or worse, the race is still always made on the course”
  • Fehlerfrei – a quick article on shooting clean
  • Shooting Efficiency comparison: First trimester 2019–20 vs. First trimester 2020–21

Categories

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

Archives

  • 2021
    • January
  • 2020
    • December
    • November
    • August
    • June
    • March
  • 2015
    • December
  • 2013
    • August
    • July
  • 2012
    • July

Search Articles

Recent Tweets

Tweets by realbiathlon

Norwegian Dominance

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

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

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

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

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

Cheers!

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

Overall performance scores, season-to-season improvements

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

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


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


Men

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

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

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

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

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

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

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


Women

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Posted in Statistical analysis | Tagged weather

Fehlerfrei – a quick article on shooting clean

Posted on 2021-01-08 | by Najtrebor | Leave a Comment on Fehlerfrei – a quick article on shooting clean

The Germans have a great word they use for shooting clean in biathlon: Fehlerfrei. The visual below is a brief look into shooting Fehlerfrei and how it relates to shooting and shot times. The interactive version allows you to filter to only men or women, by default both are included.

After reviewing shootings based on over 200,000 shots we can see just over 39% of shootings are Fehlerfrei. The men and woman have a very similar percentage.

When looking at the biathlon nations and Canada, we can see there is not much difference between Canadian men and women, but for the Swedes the women better than the men, where for the French and Norwegians the men do better than the women:

For men and women combined, the Norwegians are doing best with regards to the Fehlerfrei percentage, but the Canadians are the fastest shooters of the group.

Now let’s look at the same data split between men and women:

For the men (left) the Norwegians (red) are shooting clean over 50% of the time, but have been taking more time in the current season compared to previous seasons. The Canadians are improving from last season in both categories but are still on the low end (~35%) of the Fehlerfrei %, even though their shooting is still very fast.

The women (right) tell a different story; Germany, Sweden en Norway are heading exactly in the direction you want to go: bottom right of the chart (which means quick shooting and large percentage of Fehlerfrei shooting). In that second category Canada was in the wrong area in the last two seasons but heading in the right direction this season. Russia is going in the opposite direction.

If these trends continue this season, the Canadian women are looking promising. Although it should be noted, as shown in the first chart, the Canadians have a lot fewer shots as they have less athletes participating, so the success of individuals has a larger and more direct impact on the nation’s data/

The Tableau report contains more details and I am planning to do further analysis in R, depending on time availability. If you have any feedback or suggestions, please leave a comment below.

RJ

Posted in Statistical analysis | Tagged Fehlerfrei, shooting clean

Shooting Efficiency comparison: First trimester 2019–20 vs. First trimester 2020–21

Posted on 2021-01-07 | by real biathlon | Leave a Comment on Shooting Efficiency comparison: First trimester 2019–20 vs. First trimester 2020–21

Following up on my last post on skiing speed, this is a comparison of overall shooting quality between trimester 1 of last season and trimester 1 this winter. Shooting Efficiency is an attempt to combine shooting accuracy and shooting time. For more details how it’s calculated, see here.

If you can’t find a specific athlete, you can always look up the complete World Cup field (also available per trimester) for the ongoing season (as well as all previous seasons) here:

  • 2020–21 Shooting Efficiency: Men | Women

Note: Only athletes with at least 4 non-team races in trimester 1 of both the previous and the current season are included in the tables below. Shooting Efficiency is an overall shooting score, combining shooting accuracy and shooting time. It is the theoretical average time an athlete loses through shooting (based on hit rate, range time and potential penalty loops)


Men

Erik Lesser is the most improved shooter among regular starters, losing 2:06.6 min in a theoretical sprint at the range. He has always been fast, which hasn’t changed this year, however, his accuracy is currently at a career high (87.9%). Simon Eder is the best shooter overall (incredible 96.4% hit rate), also much improved over last year. Sturla Holm Lægreid isn’t far behind (1:56.2 min) – he doesn’t show up in the table, because his first World Cup race was in March. Johannes Thingnes Bø has been struggling with his shooting so far, his accuracy is down 4.2% (albeit on a very high level), plus he shoots 2.0s slower.

Changes in Shooting Efficiency compared to 2019–20 | World Cup Trimester 1

NoFamily NameGiven NameNationRacesHit RateRange TimePenalty LoopTime Loss
Sprint
Change
NoFamily NameGiven NameNationRacesHit RateRange TimePenalty LoopTime Loss
Sprint
Change
1LesserErikGER
987.8650.021.92:06.6-35.1
2GuigonnatAntoninFRA
990.7152.722.92:06.6-21.5
3StroliaVytautasLTU
780.0055.022.92:35.7-21.4
4EderSimonAUT
996.4349.323.11:46.9-19.4
5SamuelssonSebastianSWE
989.2953.321.32:09.4-16.6
6HasillaTomasSVK
677.5053.723.82:40.9-14.2
7MoravecOndrejCZE
987.1450.622.72:10.3-13.8
8PonsiluomaMartinSWE
981.4350.121.42:20.0-11.4
9NelinJesperSWE
975.7153.321.72:39.3-11.2
10KrcmarMichalCZE
990.0053.621.42:08.5-10.3
11WegerBenjaminSUI
986.4352.622.52:15.8-9.0
12ErmitsKalevEST
876.6753.322.92:39.9-8.4
13GaranichevEvgeniyRUS
593.7552.024.41:59.2-6.2
14DombrovskiKarolLTU
787.0056.622.72:22.7-5.6
15SinapovAntonBUL
680.0051.524.72:32.4-5.2
16HoferLukasITA
982.8651.520.02:17.4-5.1
17FakJakovSLO
992.1451.721.42:00.3-2.0
18DollBenediktGER
987.1450.822.02:09.9-1.7
19Fillon MailletQuentinFRA
992.1450.121.61:57.3-1.0
20ChristiansenVetle SjaastadNOR
990.0053.621.82:09.0-0.4
21FemlingPeppeSWE
881.6750.122.52:21.5-0.1
22NordgrenLeifUSA
681.2553.724.12:32.6-0.0
23PrymaArtemUKR
880.0050.923.12:28.1+1.4
24LatypovEduardRUS
985.0055.121.92:22.9+1.8
25BormoliniThomasITA
587.1452.921.82:13.8+1.9
26HiidensaloOlliFIN
676.2555.723.12:46.1+2.1
27ClaudeFabienFRA
980.0050.921.02:23.8+2.5
28RastorgujevsAndrejsLAT
880.8353.721.42:28.4+3.5
29DohertySeanUSA
880.0052.122.72:29.5+5.4
30JacquelinEmilienFRA
990.0049.220.41:58.7+6.1
31GowScottCAN
676.2550.723.62:37.5+6.2
32EberhardJulianAUT
878.3352.321.62:31.3+6.5
33PidruchnyiDmytroUKR
880.0049.222.52:23.5+9.2
34YaliotnauRamanBLR
769.0054.222.92:59.6+9.6
35PeifferArndGER
790.0052.421.92:06.6+10.6
36DaleJohannesNOR
987.1456.322.22:21.0+11.1
37SeppalaTeroFIN
878.3353.421.52:33.3+11.4
38DesthieuxSimonFRA
983.5751.621.52:18.7+11.6
39DovzanMihaSLO
686.2549.424.12:11.8+11.9
40TkalenkoRuslanUKR
576.6749.422.72:31.7+13.0
41KuehnJohannesGER
880.0056.020.72:33.4+13.1
42LeitnerFelixAUT
784.0058.222.62:32.6+13.3
43IlievVladimirBUL
675.0054.522.02:44.2+13.4
44BjoentegaardErlendNOR
585.7155.720.92:21.1+14.7
45VaclavikAdamCZE
673.7556.123.52:54.0+14.8
46BocharnikovSergeyBLR
880.8353.424.82:34.3+16.4
47GuzikGrzegorzPOL
772.0052.422.72:48.5+17.4
48WindischDominikITA
575.7155.220.92:41.1+17.6
49TrsanRokSLO
685.5649.823.42:13.4+18.0
50EliseevMatveyRUS
990.0049.722.52:01.8+18.5
51BoeTarjeiNOR
985.7153.520.62:16.4+18.8
52BoeJohannes ThingnesNOR
987.8652.021.32:09.8+20.8
53LangerThierryBEL
781.0054.522.32:31.5+23.5
54DudchenkoAntonUKR
680.0055.324.42:39.6+25.9
55ClaudeFlorentBEL
677.5058.521.82:46.2+29.5
56BauerKlemenSLO
774.0049.923.22:40.1+30.4
57LoginovAlexanderRUS
985.0051.722.42:16.9+30.8
58StvrteckyJakubCZE
764.0058.821.73:15.7+38.8


Women

Suvi Minkkinen is the most improved among women – she had a horrible December 2019, where she only managed to hit 66.0% of her targets. World Cup leader, Marte Olsbu Røiseland, is currently 11.9% more accurate than during trimester 1 last season. The results for Hanna Öberg (best shot overall) haven’t changed much, neither has the efficiency of Dorothea Wierer; her problems are almost exclusively skiing-related. Denise Herrmann is roughly 10s faster overall at the range (but in a sprint her 1.5% slower skiing loses her almost twice as much on the tracks).

Changes in Shooting Efficiency compared to 2019–20 | World Cup Trimester 1

NoFamily NameGiven NameNationRacesHit RateRange TimePenalty LoopTime Loss
Sprint
Change
NoFamily NameGiven NameNationRacesHit RateRange TimePenalty LoopTime Loss
Sprint
Change
1MinkkinenSuviFIN
785.0052.725.92:24.3-46.1
2FrolinaAnnaKOR
676.2555.725.72:52.4-42.3
3KadevaDanielaBUL
585.0054.927.12:30.4-32.0
4KocerginaNataljaLTU
580.0057.126.82:47.8-31.1
5DunkleeSusanUSA
878.3355.826.22:48.4-28.2
6RoeiselandMarte OlsbuNOR
988.5752.223.92:11.8-26.8
7ReidJoanneUSA
683.751:00.725.12:42.1-20.3
8ColomboCarolineFRA
881.6754.425.52:35.6-19.1
9TomingasTuuliEST
783.0058.025.32:39.0-17.2
10TachizakiFuyukoJPN
885.0059.225.92:37.2-16.2
11AlimbekavaDzinaraBLR
990.0054.724.02:13.4-15.4
12LunderEmmaCAN
991.4352.525.12:06.6-14.2
13CadurischIreneSUI
682.5049.525.82:24.3-13.4
14OebergElviraSWE
986.4353.424.52:20.1-13.0
15SemerenkoValentinaUKR
590.0052.825.62:11.2-11.6
16HerrmannDeniseGER
983.5754.223.72:27.4-10.4
17KlemencicPolonaSLO
675.0056.725.92:58.1-9.0
18HinzVanessaGER
786.0055.125.42:25.7-8.6
19SchwaigerJuliaAUT
787.0056.725.32:26.3-8.0
20KnottenKaroline OffigstadNOR
991.4351.125.52:04.0-7.8
21LescinskaiteGabrieleLTU
585.001:01.125.72:40.7-6.7
22OebergHannaSWE
990.0049.624.32:03.5-5.1
23DavidovaMarketaCZE
982.1457.124.02:37.0-4.4
24KryukoIrynaBLR
791.251:00.325.22:22.6-3.6
25Braisaz-BouchetJustineFRA
980.7156.622.92:37.5-2.7
26WiererDorotheaITA
991.4351.324.52:03.6-1.2
27Hojnisz-StaregaMonikaPOL
686.6755.725.12:24.8-1.1
28GasparinElisaSUI
782.0053.125.12:31.3-0.3
29EganClareUSA
985.0059.224.32:34.8+2.1
30HaeckiLenaSUI
879.1750.324.92:32.6+2.8
31MironovaSvetlanaRUS
678.7555.424.62:43.0+4.9
32TodorovaMilenaBUL
876.6756.824.72:51.2+5.7
33GasparinAitaSUI
784.0053.726.22:29.3+7.4
34HauserLisa TheresaAUT
984.2953.623.42:24.1+7.9
35BescondAnaisFRA
982.8657.823.62:36.1+8.0
36SimonJuliaFRA
982.8650.124.82:22.7+8.6
37BrorssonMonaSWE
885.8354.424.82:24.0+8.8
38SolaHannaBLR
870.7752.924.32:56.8+9.0
39VittozziLisaITA
982.1454.323.92:31.3+9.9
40PreussFranziskaGER
986.4351.823.62:15.7+10.7
41EckhoffTirilNOR
984.2955.723.72:28.5+11.0
42BeaudrySarahCAN
778.0052.626.42:43.4+13.2
43SanfilippoFedericaITA
575.7158.425.62:58.9+13.2
44ZukKamilaPOL
778.001:01.125.72:58.7+14.0
45EderMariFIN
673.751:01.724.93:08.7+14.1
46TandrevoldIngrid LandmarkNOR
985.7156.423.82:26.8+14.2
47OjaReginaEST
571.6753.724.52:56.9+14.4
48PuskarcikovaEvaCZE
780.0051.327.42:37.3+14.4
49JislovaJessicaCZE
778.0057.225.92:51.4+15.6
50GasparinSelinaSUI
675.0058.024.42:57.0+15.7
51BlashkoDaryaUKR
992.1456.926.02:14.1+17.0
52PerssonLinnSWE
985.0054.324.62:25.5+19.9
53DzhimaYuliiaUKR
786.0056.925.22:29.0+20.1
54VoroninaTamaraRUS
584.0052.226.62:27.1+21.2
55PidhrushnaOlenaUKR
584.2955.726.52:33.0+21.2
56ZbylutKingaPOL
774.0056.724.72:57.6+24.1
57CharvatovaLucieCZE
763.7552.123.93:10.9+28.1
58TalihaermJohannaEST
676.251:00.826.33:03.9+31.3
59ChevalierChloeFRA
879.1758.824.52:48.7+32.6
60InnerhoferKatharinaAUT
865.8355.623.93:12.8+38.0

Posted in Statistical analysis | Tagged 2019–20 season, 2020–21 season, shooting

Posts navigation

Older posts

Recent Articles

  • Norwegian Dominance
  • Overall performance scores, season-to-season improvements
  • “Whether the weather is better or worse, the race is still always made on the course”
  • Fehlerfrei – a quick article on shooting clean
  • Shooting Efficiency comparison: First trimester 2019–20 vs. First trimester 2020–21

Categories

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

Archives by Month

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

Search Articles