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Tag: results

Most improved athletes this winter

Posted on 2022-12-24 | by real biathlon | Leave a Comment on Most improved athletes this winter

Season-to-season improvements in Total Performance Scores of regular World Cup athletes. The last row of both tables shows improvement and decline in overall scores for this season’s World Cup trimester 1 compared to performances in trimester 1 last season (only athletes with at least 5 races this winter). 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

2022–23 z-Scores compared to 2021–22 | 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
1HartwegNiklasSUI
6-0.97-1.41-1.47-1.16-0.99
2AndersenFilip FjeldNOR
8-1.17-0.85-0.44-0.99-0.63
3ClaudeFlorentBEL
8-0.64-1.420.25-0.76-0.56
4GuzikGrzegorzPOL
50.07-0.070.590.09-0.50
5GiacomelTommasoITA
8-1.210.20-1.92-0.88-0.48
6LaegreidSturla HolmNOR
8-1.59-1.42-1.85-1.57-0.42
7BoeJohannes ThingnesNOR
8-1.98-0.94-1.37-1.60-0.42
8PonsiluomaMartinSWE
8-1.490.40-0.91-0.87-0.36
9DohertySeanUSA
8-0.360.20-0.89-0.26-0.36
10ReesRomanGER
8-1.06-1.04-0.40-0.98-0.35
11StvrteckyJakubCZE
6-1.110.520.34-0.46-0.33
12IlievVladimirBUL
5-0.890.640.25-0.31-0.30
13StroliaVytautasLTU
8-1.01-0.91-0.42-0.91-0.28
14SimaMichalSVK
60.44-0.860.730.10-0.26
15LapshinTimofeiKOR
5-0.29-0.36-1.91-0.50-0.25
16KomatzDavidAUT
70.00-1.360.80-0.30-0.25
17MagazeevPavelMDA
60.09-0.821.950.05-0.25
18KarlikMikulasCZE
5-0.611.040.940.05-0.23
19HiidensaloOlliFIN
8-0.54-0.56-0.52-0.54-0.22
20TachizakiMikitoJPN
60.71-1.27-0.010.05-0.20
21DollBenediktGER
8-1.41-0.27-0.71-1.00-0.17
22ClaudeFabienFRA
8-1.48-0.75-0.76-1.18-0.16
23NelinJesperSWE
8-1.24-0.750.57-0.88-0.14
24KrcmarMichalCZE
8-0.98-0.75-0.12-0.81-0.08
25BrandtOskarSWE
5-0.901.350.65-0.06-0.07
26WrightCampbellNZL
50.160.29-0.240.15-0.06
27PerrotEricFRA
5-0.810.64-0.13-0.31+0.00
28ZahknaReneEST
70.52-0.80-0.130.06+0.07
29DudchenkoAntonUKR
7-0.62-0.68-1.15-0.70+0.08
30GuigonnatAntoninFRA
8-1.09-0.37-0.34-0.79+0.12
31Fillon MailletQuentinFRA
8-1.07-1.23-1.21-1.13+0.14
32LeitnerFelixAUT
8-0.31-1.04-0.71-0.57+0.16
33RunnallsAdamCAN
60.100.11-1.77-0.12+0.17
34LangerThierryBEL
6-0.350.80-0.29-0.01+0.20
35BoeTarjeiNOR
8-1.30-0.37-0.54-0.94+0.21
36SamuelssonSebastianSWE
8-1.23-0.75-0.38-0.99+0.21
37FemlingPeppeSWE
7-0.500.22-1.39-0.40+0.21
38ChristiansenVetle SjaastadNOR
8-1.52-0.47-0.20-1.06+0.23
39SeppalaTeroFIN
7-1.030.22-0.75-0.63+0.23
40JacquelinEmilienFRA
8-1.610.11-0.89-1.02+0.24
41NawrathPhilippGER
5-0.62-0.200.21-0.40+0.31
42KuehnJohannesGER
7-1.260.67-0.47-0.60+0.32
43EderSimonAUT
5-0.41-0.67-0.52-0.50+0.32

Women

2022–23 z-Scores compared to 2021–22 | 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
1KlemencicPolonaSLO
7-0.91-0.290.20-0.59-0.68
2VittozziLisaITA
8-1.29-0.92-1.28-1.18-0.47
3KinnunenNastassiaFIN
5-0.48-0.040.56-0.23-0.46
4GasparinAitaSUI
8-0.53-0.75-0.88-0.64-0.45
5ComolaSamuelaITA
7-0.23-1.01-0.04-0.43-0.44
6SimonJuliaFRA
8-1.38-1.10-1.79-1.34-0.42
7MinkkinenSuviFIN
7-0.26-1.22-0.96-0.62-0.39
8EderMariFIN
8-1.350.46-0.09-0.67-0.37
9Batovska FialkovaPaulinaSVK
8-0.71-0.23-0.42-0.53-0.36
10TandrevoldIngrid LandmarkNOR
8-1.33-1.01-0.48-1.13-0.31
11KnottenKaroline OffigstadNOR
8-0.35-1.01-1.12-0.63-0.26
12ChevalierChloeFRA
8-0.99-0.49-0.13-0.74-0.26
13VoigtVanessaGER
8-0.92-1.360.36-0.90-0.25
14LunderEmmaCAN
6-0.54-0.76-1.49-0.72-0.25
15ZdoucDunjaAUT
70.15-1.01-0.79-0.30-0.23
16ReidJoanneUSA
6-0.17-0.130.14-0.12-0.22
17SchwaigerJuliaAUT
7-0.370.010.06-0.20-0.21
18PerssonLinnSWE
8-0.98-1.01-1.30-1.03-0.20
19MakaAnnaPOL
5-0.15-0.370.51-0.13-0.15
20TachizakiFuyukoJPN
7-0.28-1.010.79-0.36-0.13
21MagnussonAnnaSWE
8-0.71-0.84-0.82-0.76-0.12
22IrwinDeedraUSA
6-0.19-0.130.13-0.13-0.10
23WiererDorotheaITA
8-0.98-0.75-1.78-1.01-0.10
24ZukKamilaPOL
6-0.310.370.37-0.03-0.09
25TomingasTuuliEST
6-0.850.620.60-0.25-0.06
26LienIdaNOR
7-1.160.120.51-0.59-0.05
27Herrmann-WickDeniseGER
8-1.37-0.49-0.58-1.02-0.03
28KalkenbergEmilie AagheimNOR
7-0.25-0.60-0.79-0.42-0.03
29DavidovaMarketaCZE
8-1.02-0.92-1.20-1.01-0.00
30BendikaBaibaLAT
7-0.830.73-0.66-0.36+0.02
31Chevalier-BouchetAnaisFRA
8-1.22-0.49-1.13-1.00+0.04
32Haecki-GrossLenaSUI
8-0.940.46-1.09-0.55+0.04
33OebergElviraSWE
8-1.72-0.84-0.86-1.36+0.06
34HauserLisa TheresaAUT
8-0.93-0.84-1.44-0.97+0.16
35LieLotteBEL
7-0.38-1.22-0.13-0.59+0.17
36TodorovaMilenaBUL
7-0.540.32-0.32-0.26+0.19
37OebergHannaSWE
8-1.31-0.14-1.51-1.00+0.24
38PreussFranziskaGER
5-0.81-0.31-1.13-0.70+0.33
39StremousAlinaMDA
7-0.16-0.091.230.03+0.33
40JislovaJessicaCZE
7-0.15-0.70-0.49-0.35+0.40
41CharvatovaLucieCZE
6-0.401.25-0.880.02+0.42
42BlashkoDariaUKR
60.48-0.51-0.450.08+0.42
43FialkovaIvonaSVK
5-0.731.73-0.180.05+0.45
44BilosiukOlenaUKR
50.68-0.69-0.030.20+0.53
45NilssonStinaSWE
7-0.690.630.47-0.17+0.54

Posted in Statistical analysis | Tagged results, shooting, skiing

Olympic medals in biathlon (1960 – 2018)

Posted on 2022-01-12 | by real biathlon | Leave a Comment on Olympic medals in biathlon (1960 – 2018)

All medals (men and women)

Individual gold medals (men and women)

Posted in Biathlon Media, Long-term trends | Tagged results

Biathlon World Cup wins by age | Men

Posted on 2021-12-16 | by real biathlon | Leave a Comment on Biathlon World Cup wins by age | Men

Men’s individual/non-team wins in World Cup level races by age. Biathlon World Cup, World Championships, Olympics – Women (1958 – 2021).

Posted in Biathlon Media, Long-term trends | Tagged Data, data visualization, results

Projection for the season opener

Posted on 2020-11-21 | by real biathlon | Leave a Comment on Projection for the season opener

The many statistics collected on this site allow to calculate a theoretical race time, solely based on performance data. I thought this might be an interesting exercise for the season opening 15/20km individuals – plus it’s a simple reminder where we left off. These aren’t meant to be serious predictions, of course. The individual is arguably the most unpredictable discipline and produces the most surprises anyway. Not to mention a lot usually changes during the off-season, maybe even more so this year.

Note: Projected times are calculated based on ski speed, hit rates and range times in last season’s three individuals (IN). Hit rates are rounded to the nearest full shot. The top 30 median Course Time at the 2015 Kontiolahti IN is used as reference (last IN held in Kontiolahti) and multiplied with last season’s IN ski speed in percent. The “Time Loss Shooting” column follows the idea of the Shooting Efficiency score.

Men 20 km Individual

Johannes Thingnes Bø comes out on top, “winning” by a margin of over one minute, which probably isn’t surprising, especially since Martin Fourcade is no longer there. However, he’s projected to win mostly due to his extremely high 93.3% hit rate (rounded to 19/20 hits for this), not because of his ski speed, which wasn’t that remarkable in this event last winter. Quentin Fillon Maillet and Tarjei Bø are second and third – both skied faster, but were less accurate at the shooting range in 2019–20 individuals.

Race Projection based on 2019–20 IN statistics

RankFamily NameGiven NameNationRacesback from
Top30 median
(in %)
Projected
Course Time
Total
hit rate
(in %)
Projected
Time Loss
Shooting
Projected
Total
Race Time
Behind
RankFamily NameGiven NameNationRacesback from
Top30 median
(in %)
Projected
Course Time
Total
hit rate
(in %)
Projected
Time Loss
Shooting
Projected
Total
Race Time
Behind
1BoeJohannes ThingnesNOR
3-1.7143:23.895.004:30.447:54.2
2Fillon MailletQuentinFRA
3-3.8042:28.585.006:27.948:56.3+1:02.1
3BoeTarjeiNOR
3-1.7943:21.690.005:42.449:04.0+1:09.8
4ClaudeFabienFRA
3-0.8843:45.690.005:31.949:17.5+1:23.3
5JacquelinEmilienFRA
2-0.1544:05.190.005:27.349:32.4+1:38.3
6DollBenediktGER
3-1.5943:26.885.006:24.249:51.0+1:56.8
7EliseevMatveyRUS
2+1.0544:36.890.005:20.449:57.3+2:03.1
8WegerBenjaminSUI
3+0.1044:11.690.005:46.349:57.9+2:03.7
9NawrathPhilippGER
2+0.7944:29.990.005:37.450:07.3+2:13.1
10PidruchnyiDmytroUKR
2+1.2844:42.990.005:24.550:07.4+2:13.2
11LoginovAlexanderRUS
3-0.7943:48.285.006:19.850:08.0+2:13.8
12HoferLukasITA
3-1.6843:24.685.006:43.850:08.4+2:14.2
13DesthieuxSimonFRA
3-0.7743:48.585.006:25.950:14.4+2:20.2
14FakJakovSLO
3+1.8644:58.490.005:25.050:23.4+2:29.2
15DaleJohannesNOR
3-1.0043:42.685.006:47.150:29.7+2:35.6
16EderSimonAUT
3+2.3045:09.890.005:23.850:33.6+2:39.4
17MoravecOndrejCZE
3+2.2945:09.790.005:27.750:37.4+2:43.2
18HornPhilippGER
3+0.1344:12.485.006:30.550:42.9+2:48.7
19BjoentegaardErlendNOR
2+0.0144:09.385.006:38.250:47.5+2:53.3
20SamuelssonSebastianSWE
2+2.4845:14.690.005:38.550:53.2+2:59.0
21GaranichevEvgeniyRUS
3+1.2444:42.085.006:29.451:11.3+3:17.1
22PrymaArtemUKR
3+1.2344:41.585.006:37.751:19.2+3:25.0
23KuehnJohannesGER
3-0.9943:42.880.007:43.051:25.8+3:31.6
24NordgrenLeifUSA
3+4.0545:56.390.005:36.051:32.3+3:38.1
25RastorgujevsAndrejsLAT
3-0.5943:53.380.007:47.251:40.5+3:46.3
26ClaudeFlorentBEL
3+3.5145:42.090.006:04.851:46.8+3:52.6
27EberhardJulianAUT
3+0.9344:33.680.007:16.751:50.3+3:56.1
28LesserErikGER
2+5.1846:26.290.005:24.551:50.8+3:56.6
29LatypovEduardRUS
3+2.5645:16.785.006:37.851:54.5+4:00.3
30DombrovskiKarolLTU
3+6.5447:02.295.004:56.451:58.7+4:04.5

Women 15 km Individual

Olympic champion Hanna Öberg failed to win an individual last season, however, she still won the discipline World Cup title; her as the projected winner is no surprise either. Marte Olsbu Røiseland and Monika Hojnisz-Staręga round out this theoretical podium. What’s maybe most noteworthy is the fact that eight athletes are inside a minute of the winning time (none for the men) – rather emblematic of the gender divide when it comes to competitiveness at the very top of the field in the last couple of seasons.

Race Projection based on 2019–20 IN statistics

RankFamily NameGiven NameNationRacesback from
Top30 median
(in %)
Projected
Course Time
Total
hit rate
(in %)
Projected
Time Loss
Shooting
Projected
Total
Race Time
Behind
RankFamily NameGiven NameNationRacesback from
Top30 median
(in %)
Projected
Course Time
Total
hit rate
(in %)
Projected
Time Loss
Shooting
Projected
Total
Race Time
Behind
1OebergHannaSWE
3-0.9538:43.790.005:20.844:04.5
2RoeiselandMarte OlsbuNOR
3-2.6438:04.285.006:31.644:35.8+31.3
3Hojnisz-StaregaMonikaPOL
3-0.8638:45.890.005:52.344:38.1+33.6
4HerrmannDeniseGER
3-2.9737:56.385.006:42.544:38.8+34.3
5WiererDorotheaITA
3-1.6038:28.485.006:22.044:50.4+45.9
6KuklinaLarisaRUS
3+1.4739:40.490.005:17.344:57.7+53.2
7PreussFranziskaGER
3+1.3039:36.590.005:21.544:57.9+53.4
8BraisazJustineFRA
3-2.8237:59.985.007:00.044:59.9+55.4
9HinzVanessaGER
3+0.9339:27.990.005:38.445:06.3+1:01.7
10DzhimaYuliiaUKR
3+1.4039:38.790.005:41.045:19.7+1:15.2
11StarykhIrinaRUS
2+1.9439:51.690.005:41.945:33.5+1:29.0
12DavidovaMarketaCZE
3-1.3438:34.585.007:07.245:41.8+1:37.3
13BrorssonMonaSWE
2+2.4340:03.190.005:40.145:43.3+1:38.8
14LunderEmmaCAN
2+3.4540:26.990.005:20.445:47.3+1:42.8
15TandrevoldIngrid LandmarkNOR
3-0.1839:01.885.006:54.145:55.9+1:51.4
16VittozziLisaITA
3+0.7839:24.385.006:39.346:03.6+1:59.1
17EckhoffTirilNOR
3-2.0138:18.880.007:52.746:11.5+2:07.0
18SimonJuliaFRA
3-0.4438:55.880.007:17.346:13.1+2:08.6
19Yurlova-PerchtEkaterinaRUS
3+1.4239:39.385.006:35.046:14.3+2:09.8
20Kristejn PuskarcikovaEvaCZE
3+3.9340:38.390.005:45.546:23.7+2:19.2
21TodorovaMilenaBUL
2+4.2640:45.890.005:58.546:44.3+2:39.8
22EganClareUSA
3+1.9439:51.485.006:56.946:48.3+2:43.8
23HauserLisa TheresaAUT
3+3.1240:19.385.006:31.146:50.4+2:45.8
24BescondAnaisFRA
3+0.0239:06.680.007:44.346:50.9+2:46.4
25GasparinAitaSUI
3+3.0840:18.385.006:33.546:51.8+2:47.3
26FialkovaPaulinaSVK
2+0.0139:06.280.007:46.446:52.6+2:48.1
27MerkushynaAnastasiyaUKR
3+4.1540:43.485.006:18.547:02.0+2:57.5
28RiederChristinaAUT
3+5.9141:24.790.005:52.047:16.8+3:12.3
29SchwaigerJuliaAUT
3+4.0940:41.885.006:37.947:19.7+3:15.2
30ZdoucDunjaAUT
2+6.9741:49.490.005:33.047:22.5+3:17.9
Posted in Statistical analysis | Tagged 2020–21 season, projection, results

World Cup point distribution

Posted on 2013-08-09 | by real biathlon | Leave a Comment on World Cup point distribution

Last season was quite unusual. Martin Fourcade and Tora Berger won the overall titles with record scores and record gaps to second place. How do the 2012–13 World Cup points compare to previous years and how has the point distribution developed since 2001–02?
Note: The World Cup points system was changed after 2007–08 (50, 46, 43, … → 60, 54, 48, …). Plus several minor changes in the number of dropped worst results.

The chart above shows how much of the possible World Cup points (last year: 1440p = 60p*24, 2 dropped scores) the season’s top 6 have won. Fourcade set a new record, claiming 1248 points (86.7 %), surpassing Raphael Poiree‘s previous mark, who had won 81.3 % of all possible points in 2003–04. Fourcade’s gap to second place was also unprecedented last year: 421 points, or 50.1 % more than Emil Hegle Svendsen.

Naturally, the 2008–09 season brought some big changes: with 10 more athletes awarded World Cup points, the share of points won by the men’s top 30 or top 40 decreased by roughly 10 % in one year. In general, the top athletes win slightly less points today than they did in 2001–02. The biggest differences in the share of won points occurred for the season’s top 10 (43.4 % → 32.3 %, -11.1 %) and top 20 (65.5 % →55.6 %, -9.9 %).

Tora Berger set a new record last season as well, winning 85.7 % of all points available. She beat Magdalena Neuner‘s record (84.4 %) from one year earlier. There are huge differences between the last two seasons though: 2011–12 was a hard fought battle between four (!) athletes who won at least 70 % of the max points, in 2012–13 second-placed Darya Domracheva only claimed 64 %. 

The points for the top female athletes didn’t decline as much as for the men. In 2001–02 the top 10 won 44.4 % of all points, in 2012–13 it was 37.1 % (-7.3 %). The top women win a slightly bigger share of all World Cup points (3-5 %), indicating weaker competition from lower ranked athletes. 

Generally, World Cup points are shared more broadly today compared to 12 years ago, which might point to more depth in World Cup fields. However, at least part of that is due to the changed points system. The 2012–13 season was extremely lopsided (for men and women alike); hopefully not the start of a trend but only a one-time anomaly.

Posted in Long-term trends | Tagged results

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