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Year: 2021

All-time records for World Cup level pursuits

Posted on 2021-02-15 | by real biathlon | Leave a Comment on All-time records for World Cup level pursuits

At every major championship, there’s always the question of all-time records for pursuit performances. Let’s take a more detailed look at this. Pursuits are held since 1996–97 on World Cup level; there have been 185 men’s and 184 women’s pursuits in total. Records for a few of the earliest pursuits (1996–97 and 1997–98) don’t have bib numbers and/or time deficits and couldn’t be included here.

Men

Andreas Birnbacher holds the record for winning from furthest back (starting in 26th position), while Sven Fischer won with the biggest time gap at the start (1:36 min). Tarjei Bø once made second place with bib 44, which is a record for both biggest position and biggest time gain for a podium finisher (it’s also the biggest time gain overall, 2:14.4). Julien Robert has the record for the biggest improvement overall (50 positions).

Win from furthest back (position)

RankBibFamily NameGiven NameNationYearLocationRank Diff.Start Deficit
126BirnbacherAndreasGER
2011Hochfilzen-251:09.0
120DrachevVladimirBLR
2003Oestersund-191:18.0
118SamuelssonSebastianSWE
2020Kontiolahti-171:10.0
117SumannChristophAUT
2007Pokljuka-160:59.0
116BjoerndalenOle EinarNOR
2006Kontiolahti-150:48.0
115PoireeRaphaelFRA
1999Ruhpolding-140:47.0
113MaigourovViktorRUS
2001Oberhof-121:32.0
112MaigourovViktorRUS
1997Brezno-Osrblie (WCH)-110:48.0
111PoireeRaphaelFRA
1999Lake Placid-100:52.0
110FischerSvenGER
2004Oslo -91:36.0

Win from furthest back (time deficit)

RankBibFamily NameGiven NameNationYearLocationRank Diff.Start Deficit
110FischerSvenGER
2004Oslo -91:36.0
113MaigourovViktorRUS
2001Oberhof-121:32.0
120DrachevVladimirBLR
2003Oestersund-191:18.0
19BoeufAlexisFRA
2011Presque Isle ME-81:14.0
18FerryBjoernSWE
2010Whistler-71:12.0
118SamuelssonSebastianSWE
2020Kontiolahti-171:10.0
126BirnbacherAndreasGER
2011Hochfilzen-251:09.0
17BjoerndalenOle EinarNOR
2003Oslo -61:07.0
16SvendsenEmil HegleNOR
2011Oslo -51:04.0
110GreisMichaelGER
2008PyeongChang-91:01.0

Podium from furthest back (position)

RankBibFamily NameGiven NameNationYearLocationRank Diff.Start DeficitBehind (Finish)Time Gain
244BoeTarjeiNOR
2011Oslo-422:15.00:00.62:14.4
335FourcadeMartinFRA
2011Oslo-321:57.00:07.31:49.7
329KrcmarMichalCZE
2017Ruhpolding-261:33.00:19.51:13.5
126BirnbacherAndreasGER
2011Hochfilzen-251:09.0 1:09.0
325GrossRiccoGER
2001Hochfilzen-222:38.00:26.32:11.7
223ShipulinAntonRUS
2017PyeongChang-211:30.00:34.50:55.5
323ShipulinAntonRUS
2012Oestersund-201:08.00:03.31:04.7
222FourcadeSimonFRA
2013Khanty-Mansiysk-201:32.00:35.70:56.3
323KruglovNikolayRUS
2004Fort Kent -202:20.01:27.40:52.6
120DrachevVladimirBLR
2003Oestersund-191:18.0 1:18.0

Podium from furthest back (time deficit)

RankBibFamily NameGiven NameNationYearLocationRank Diff.Start DeficitBehind (Finish)Time Gain
244BoeTarjeiNOR
2011Oslo-422:15.00:00.62:14.4
325GrossRiccoGER
2001Hochfilzen-222:38.00:26.32:11.7
335FourcadeMartinFRA
2011Oslo-321:57.00:07.31:49.7
110FischerSvenGER
2004Oslo-91:36.0 1:36.0
113MaigourovViktorRUS
2001Oberhof-121:32.0 1:32.0
321PuurunenPaavoFIN
2003Khanty-Mansiysk (WCH)-182:16.00:56.31:19.7
314WolfAlexanderGER
2005Oberhof-111:25.00:06.51:18.5
120DrachevVladimirBLR
2003Oestersund-191:18.0 1:18.0
316AndresenFrodeNOR
2000Lahti-131:37.00:22.81:14.2
19BoeufAlexisFRA
2011Presque Isle -81:14.0 1:14.0

Biggest improvement overall (position)

RankBibFamily NameGiven NameNationYearLocationRank Diff.Start DeficitBehind (Finish)Time Gain
1060RobertJulienFRA
2005Oestersund-501:42.00:57.80:44.2
957PrymaArtemUKR
2017Oberhof-482:28.01:55.30:32.7
1158CattarinussiReneITA
1999Ruhpolding-471:52.00:52.70:59.3
956KruglovNikolayRUS
2006Hochfilzen-472:34.02:59.7-0:25.7
1459DostalRomanCZE
2003Hochfilzen-452:22.02:34.7-0:12.7
1256RostovtsevPavelRUS
1996Oslo -442:17.01:38.60:38.4
1760 Bailly-SalinsPatriceFRA
1996Oslo -432:25.02:08.80:16.2
1457HoferLukasITA
2015Khanty-Mansiysk-432:00.01:48.80:11.2
851TchepikovSergeiRUS
2002Oestersund-432:07.01:04.51:02.5
1052SamuelssonSebastianSWE
2018Nove Mesto-422:26.01:47.10:38.9

Biggest improvement overall (time)

RankBibFamily NameGiven NameNationYearLocationRank Diff.Start DeficitBehind (Finish)Time Gain
244BoeTarjeiNOR
2011Oslo -422:15.00:00.62:14.4
325GrossRiccoGER
2001Hochfilzen-222:38.00:26.32:11.7
642BjoerndalenOle EinarNOR
2000Lahti-362:41.00:33.72:07.3
335FourcadeMartinFRA
2011Oslo -321:57.00:07.31:49.7
2558MesotitschDanielAUT
2001Hochfilzen-334:00.02:23.91:36.1
110FischerSvenGER
2004Oslo -91:36.0 1:36.0
48RostovtsevPavelRUS
2001Hochfilzen-41:59.00:27.01:32.0
113MaigourovViktorRUS
2001Oberhof-121:32.0 1:32.0
1143RozhkovSergeiRUS
2004Oslo -322:30.00:58.61:31.4
948BjoerndalenOle EinarNOR
2007Khanty-Mansiysk-392:00.00:29.61:30.4

The biggest drops of all time: Vladimir Drachev (50 positions, Lillehammer 1996) and Jakov Fak (45 positions, Hochfilzen 2011).


Women

Martina Beck won from furthest back on the women’s side (bib 15), Magdalena Forsberg once overcame a deficit of 1:46 min (she started in second place in that race however). For podiums finishers, Florence Baverel-Robert and Olga Romasko hold the records for most positions and most time gained. In terms of overall improvement, Darya Domracheva has both records (48 positions, 2:30.9 time gain).

Win from furthest back (position)

RankBibFamily NameGiven NameNationYearLocationRank Diff.Start Deficit
115BeckMartinaGER
2000Oestersund-141:29.0
114BergerToraNOR
2013Antholz-Anterselva-131:08.0
111SimonJuliaFRA
2020Kontiolahti-100:57.0
110BeckMartinaGER
2003Khanty-Mansiysk (WCH)-90:58.0
110MakarainenKaisaFIN
2015Oestersund-90:52.0
110HenkelAndreaGER
2009Trondheim-90:43.0
19DomrachevaDaryaBLR
2014Sochi (Olympics)-80:32.0
18MedvedtsevaOlgaRUS
2002Soldier Hollow (Olympics)-71:03.0
18WilhelmKatiGER
2005Khanty-Mansiysk-71:03.0
18EckhoffTirilNOR
2020Kontiolahti-71:01.0

Win from furthest back (time deficit)

RankBibFamily NameGiven NameNationYearLocationRank Diff.Start Deficit
12ForsbergMagdalenaSWE
2001Oslo -11:46.0
115BeckMartinaGER
2000Oestersund-141:29.0
114BergerToraNOR
2013Antholz-Anterselva-131:08.0
17ForsbergMagdalenaSWE
2001Soldier Hollow-61:05.0
18MedvedtsevaOlgaRUS
2002Soldier Hollow (Olympics)-71:03.0
18WilhelmKatiGER
2005Khanty-Mansiysk-71:03.0
18EckhoffTirilNOR
2020Kontiolahti-71:01.0
110BeckMartinaGER
2003Khanty-Mansiysk (WCH)-90:58.0
111SimonJuliaFRA
2020Kontiolahti-100:57.0
15EkholmHelenaSWE
2009PyeongChang (WCH)-40:55.0

Podium from furthest back (position)

RankBibFamily NameGiven NameNationYearLocationRank Diff.Start DeficitBehind (Finish)Time Gain
332BaverelFlorenceFRA
2000Oslo-292:08.00:42.41:25.6
227DomrachevaDaryaBLR
2017Hochfilzen (WCH)-251:26.00:11.61:14.4
322SemerenkoVitaUKR
2018Oberhof-191:38.01:10.20:27.8
322BeckMartinaGER
2004Beitostolen-191:00.00:25.50:34.5
221ZubrilovaOlenaUKR
2003Oslo-191:00.00:03.60:56.4
321SolemdalSynnoeveNOR
2014Oberhof-181:55.01:11.70:43.3
220DomrachevaDaryaBLR
2014Kontiolahti-181:07.01:00.00:07.0
220WilhelmKatiGER
2005Oestersund-181:15.00:13.31:01.7
220WiererDorotheaITA
2015Oestersund-181:11.00:01.91:09.1
319BaillySandrineFRA
2004Lake Placid -161:26.00:51.00:35.0

Podium from furthest back (time deficit)

RankBibFamily NameGiven NameNationYearLocationRank Diff.Start DeficitBehind (Finish)Time Gain
313RomaskoOlgaRUS
1996Oslo-103:25.01:12.12:12.9
23KouklevaGalinaRUS
1996Oslo-12:35.00:34.12:00.9
12ForsbergMagdalenaSWE
2001Oslo-11:46.0 1:46.0
37BeckMartinaGER
2001Oslo-42:13.00:41.41:31.6
115BeckMartinaGER
2000Oestersund-141:29.0 1:29.0
332BaverelFlorenceFRA
2000Oslo-292:08.00:42.41:25.6
215BaillySandrineFRA
2005Khanty-Mansiysk-131:34.00:19.01:15.0
227DomrachevaDaryaBLR
2017Hochfilzen (WCH)-251:26.00:11.61:14.4
313ForsbergMagdalenaSWE
1999Oberhof-101:34.00:23.11:10.9
218GrubbenLindaNOR
2007Antholz-Anterselva (WCH)-161:17.00:07.11:09.9

Biggest improvement overall (position)

RankBibFamily NameGiven NameNationYearLocationRank Diff.Start DeficitBehind (Finish)Time Gain
553DomrachevaDaryaBLR
2009PyeongChang (WCH)-483:10.00:39.12:30.9
552HolubcovaKaterinaCZE
2003Hochfilzen-472:44.01:06.31:37.7
752BrunetMarie LaureFRA
2009PyeongChang (WCH)-453:09.00:54.92:14.1
1457TandrevoldIngrid LandmarkNOR
2020Antholz-Anterselva (WCH)-432:01.01:30.10:30.9
1759HoegbergElisabethSWE
2015Hochfilzen-421:55.01:12.20:42.8
1859SemerenkoValentinaUKR
2020Ruhpolding-412:25.02:03.10:21.9
1960TofalviEvaROU
2015Oestersund-412:21.02:19.30:01.7
1757LiuXianyingCHN
2004Oestersund-402:24.02:49.0-0:25.0
1959TakahashiRyokoJPN
2000Oberhof-403:44.05:04.4-1:20.4
645PreussFranziskaGER
2019Oberhof-391:49.00:54.70:54.3

Biggest improvement overall (time)

RankBibFamily NameGiven NameNationYearLocationRank Diff.Start DeficitBehind (Finish)Time Gain
553DomrachevaDaryaBLR
2009PyeongChang (WCH)-483:10.00:39.12:30.9
752BrunetMarie LaureFRA
2009PyeongChang (WCH)-453:09.00:54.92:14.1
313RomaskoOlgaRUS
1996Oslo -103:25.01:12.12:12.9
23KouklevaGalinaRUS
1996Oslo -12:35.00:34.12:00.9
1125BriandAnneFRA
1996Oslo -143:55.02:03.51:51.5
12ForsbergMagdalenaSWE
2001Oslo -11:46.0 1:46.0
552HolubcovaKaterinaCZE
2003Hochfilzen-472:44.01:06.31:37.7
610TalanovaNadejdaRUS
1996Oslo -43:10.01:32.91:37.1
912ParamyguinaSvetlanaBLR
1996Oslo -33:24.01:47.41:36.6
48NiogretCorinneFRA
1996Oslo -42:58.01:22.31:35.7

The biggest drops of all time: Franziska Hildebrand (47 positions, Nove Mesto 2015) and Svetlana Mironova (46 positions, Hochfilzen 2017).

Posted in Statistical analysis | Tagged pursuit

Favorites for the 2021 Biathlon World Championships

Posted on 2021-02-06 | by real biathlon | Leave a Comment on Favorites for the 2021 Biathlon World Championships

With the World Championships around the corner, it’s worth looking at the best-performing athletes after Christmas again. I think it’s fair to assume that the majority of medal winners will come from in-form athletes going into the championship, although history tells us there are a handful of surprises usually. Below, I listed the best biathletes (ranked by Overall Performance Scores) for the January races – full results for the entire field here: men & women.


Note: Only athletes with at least 3 races in January are included. 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

World Cup leader Johannes Thingnes Bø tops the ranking unsurprisingly; he won 3 out of 6 races after Christmas and set the top ski time in 5 of them. However, his overall score is down from last season, because his non-team shooting percentage is 6.3% lower. Sturla Holm Lægreid‘s hit rate also declined slightly in January, but it still stands at an incredible 92.9%, giving him 4 wins this season. Even though Lukas Hofer failed to make a single podium so far, he has been very consistent; the fourth-fastest skier overall and top 6 in all but one of the races in trimester 2.

Quentin Fillon Maillet‘s average race rank wouldn’t tell you (forgetting a penalty loop in Oberhof is to blame for that), but he has been best of the rest behind the Norwegians this season (4 podiums in total, including 2 in the last 2 races in Antholz). Tarjei Bø is currently at his best ski speed since 2010–11 (third-fastest overall), but he is also the slowest shooter in the top 10. Arnd Peiffer‘s hit rate of 90.0% this winter is a tied career-best for him, Johannes Dale is the field’s second-fastest skier (both for the season and January-only).

The other two race winners, Alexander Loginov and Sebastian Samuelsson, come to Pokljuka with some question marks. Reigning sprint world champion Loginov only had one top 6 result all season, his win in Antholz, while Samuelsson’s ski form decline Dec. to Jan. is the worst of the men’s field. The other multiple podium finishers this winter are: Émilien Jacquelin, Martin Ponsiluoma and Fabien Claude (who also saw a big decline in January).

Top 50 Overall performance scores (z-Scores) | Non-Team events January 2021

NoFamily NameGiven NameNationRacesRank
(avg)
Points
(avg)
Ski Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
NoFamily NameGiven NameNationRacesRank
(avg)
Points
(avg)
Ski Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
1BoeJohannes ThingnesNOR
64.746.8-1.98-0.35-0.70-1.36
2LaegreidSturla HolmNOR
65.745.3-1.30-1.17-1.23-1.25
3HoferLukasITA
66.338.8-1.45-0.65-0.84-1.15
4Fillon MailletQuentinFRA
521.433.4-1.36-0.55-1.55-1.15
5BoeTarjeiNOR
67.740.8-1.47-0.86-0.23-1.14
6PeifferArndGER
614.529.8-1.24-1.06-0.62-1.11
7DaleJohannesNOR
610.734.2-1.60-0.45-0.14-1.09
8PonsiluomaMartinSWE
611.230.3-1.42-0.04-1.34-1.01
9DollBenediktGER
615.825.5-1.29-0.35-1.13-1.00
10LoginovAlexanderRUS
515.429.6-0.99-1.06-0.66-0.97
11JacquelinEmilienFRA
615.225.8-1.23-0.35-1.00-0.95
12FakJakovSLO
615.527.7-0.82-1.06-1.23-0.94
13SamuelssonSebastianSWE
619.223.0-0.96-0.86-0.51-0.87
14LesserErikGER
620.022.8-0.82-0.76-1.34-0.87
15WegerBenjaminSUI
615.527.2-0.75-1.17-0.69-0.86
16DesthieuxSimonFRA
615.825.3-1.07-0.35-1.00-0.85
17ChristiansenVetle SjaastadNOR
521.626.0-1.01-0.55-0.53-0.82
18BjoentegaardErlendNOR
414.327.3-0.80-1.06-0.28-0.82
19EderSimonAUT
619.024.0-0.45-1.37-1.29-0.82
20EliseevMatveyRUS
515.225.0-0.64-1.06-0.86-0.79
21YaliotnauRamanBLR
329.318.0-1.13-0.30-0.08-0.76
22PidruchnyiDmytroUKR
438.511.4-1.070.13-0.89-0.70
23WindischDominikITA
533.014.2-1.110.09-0.47-0.69
24HornPhilippGER
433.010.3-1.18-0.040.21-0.68
25SmolskiAntonBLR
432.58.8-0.76-0.55-0.53-0.67
26PrymaArtemUKR
432.810.0-0.68-0.55-0.84-0.66
27SeppalaTeroFIN
434.38.0-1.07-0.040.21-0.62
28LeitnerFelixAUT
625.716.5-0.82-0.550.28-0.61
29BocharnikovSergeyBLR
534.014.8-0.58-0.68-0.48-0.59
30ClaudeFabienFRA
630.518.3-0.920.26-0.82-0.56
31FinelloJeremySUI
535.09.4-1.060.34-0.20-0.55
32GuigonnatAntoninFRA
626.213.5-0.900.26-0.70-0.54
33GiacomelTommasoITA
443.52.5-0.700.30-1.60-0.52
34RastorgujevsAndrejsLAT
533.28.4-0.900.21-0.26-0.50
35NelinJesperSWE
340.05.8-1.170.161.18-0.50
36DohertySeanUSA
439.84.0-0.36-0.72-0.60-0.50
37LatypovEduardRUS
624.516.7-0.970.260.04-0.49
38ReesRomanGER
328.313.7-0.55-0.650.31-0.47
39KomatzDavidAUT
622.817.0-0.11-1.370.03-0.46
40GowChristianCAN
445.39.8-0.22-0.55-1.30-0.44
41DudchenkoAntonUKR
326.317.3-0.01-1.27-0.38-0.42
42GowScottCAN
448.50.8-0.16-0.38-1.42-0.38
43BormoliniThomasITA
442.09.5-0.52-0.210.01-0.37
44DombrovskiKarolLTU
447.84.5-0.05-0.890.15-0.27
45NordgrenLeifUSA
446.30.50.14-1.06-0.07-0.23
46StreltsovKirillRUS
443.36.3-0.26-0.21-0.06-0.22
47StroliaVytautasLTU
442.08.8-0.410.13-0.01-0.21
48StvrteckyJakubCZE
458.81.5-0.860.641.07-0.20
49FemlingPeppeSWE
537.45.00.03-0.42-0.67-0.19
50ClaudeEmilienFRA
454.82.5-0.10-0.38-0.03-0.17


Women

Lisa Theresa Hauser won most World Cup points in January, something few would have predicted before Christmas. Being both accurate (93.0%) and fast (26.2s) makes her the most efficient shot on the women’s side at the moment. Combined with her new-found top10 ski speed, she is a threat in all events. Tiril Eckhoff followed her trajectory from last season almost exactly: four incredible World Cups in a row, followed by a considerable dip in form. Eckhoff has most wins (6) and podiums (8), but will her disappointing showing in Antholz last year be on her mind?

World Cup leader Marte Olsbu Røiseland has made only one podium in trimester 2, however, she remains virtually tied with Eckhoff as the field’s top skier. Hanna Öberg managed two wins and the second-most podiums this winter (7). Julia Simon, who alongside Öberg is the fastest shooter, won two of the last 4 races, still, she is arguably the least consistent (last four positions: 59-1-62-1). Monika Hojnisz-Staręga comes 5th, but she only did 3 races (2 of them sprints), which inflates her ski speed for this ranking. Nonetheless, she is much improved, winning the European Championship Individual recently.

Dorothea Wierer‘s ski speed improved after Christmas (still off her pace from the last two seasons); in contrast, Franziska Preuß is skiing faster than ever. Anaïs Chevalier-Bouchet‘s statistics stand out in particular: her hit rate is roughly 6% lower than in any of her previous five seasons, at the same time she currently skies roughly 1.5% faster than ever before. The two other multiple podium finishers, Dzinara Alimbekava and Elvira Öberg, have both been struggling in January (no top 6 result).

Top 50 Overall performance scores (z-Scores) | Non-Team events January 2021

NoFamily NameGiven NameNationRacesRank
(avg)
Points
(avg)
Ski Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
NoFamily NameGiven NameNationRacesRank
(avg)
Points
(avg)
Ski Speed
Score
Hit Rate
Score
Range Time
Score
Total
Performance
Score
1HauserLisa TheresaAUT
64.246.8-1.09-1.26-1.62-1.20
2EckhoffTirilNOR
66.245.2-1.48-0.51-1.24-1.17
3RoeiselandMarte OlsbuNOR
610.035.0-1.44-0.51-0.82-1.10
4OebergHannaSWE
68.539.7-0.93-0.79-2.00-1.02
5Hojnisz-StaregaMonikaPOL
314.727.3-1.14-0.98-0.47-1.01
6WiererDorotheaITA
611.234.2-0.88-0.89-1.83-1.00
7MironovaSvetlanaRUS
610.732.2-1.05-0.60-1.48-0.97
8PreussFranziskaGER
612.832.2-1.00-0.70-1.28-0.95
9DavidovaMarketaCZE
614.227.8-1.22-0.51-0.38-0.91
10DzhimaYuliiaUKR
512.032.0-0.82-1.10-0.57-0.87
11SimonJuliaFRA
623.731.5-1.280.52-1.93-0.84
12Braisaz-BouchetJustineFRA
615.027.3-1.16-0.32-0.47-0.83
13PavlovaEvgeniyaRUS
519.423.2-0.76-0.51-0.97-0.71
14HerrmannDeniseGER
620.020.2-1.360.52-0.37-0.70
15KaishevaUlianaRUS
618.322.5-0.54-0.79-1.22-0.69
16PerssonLinnSWE
618.822.2-0.75-0.890.16-0.68
17HaeckiLenaSUI
432.09.0-0.58-0.51-1.45-0.66
18BescondAnaisFRA
616.024.8-0.83-0.600.10-0.65
19AlimbekavaDzinaraBLR
619.320.3-0.88-0.32-0.26-0.64
20HettichJaninaGER
621.520.7-0.52-1.07-0.11-0.63
21GasparinSelinaSUI
434.010.5-1.10-0.040.30-0.63
22HinzVanessaGER
523.018.0-0.57-0.86-0.33-0.63
23BendikaBaibaLAT
436.08.5-1.200.43-0.34-0.63
24Chevalier-BouchetAnaisFRA
611.531.0-0.88-0.14-0.45-0.61
25KuklinaLarisaRUS
522.418.6-0.27-0.98-1.29-0.60
26SolaHannaBLR
435.56.3-1.240.89-0.91-0.58
27OebergElviraSWE
623.818.2-0.61-0.42-0.83-0.58
28KruchinkinaElenaBLR
527.419.0-1.210.310.80-0.53
29LienIdaNOR
438.08.5-0.990.110.45-0.50
30CadurischIreneSUI
441.34.8-0.25-0.51-1.65-0.49
31EderMariFIN
342.02.8-1.06-0.041.27-0.48
32LunderEmmaCAN
533.011.5-0.23-0.63-1.36-0.48
33ColomboCarolineFRA
431.810.0-0.46-0.51-0.28-0.45
34KnottenKaroline OffigstadNOR
628.212.30.18-1.45-1.05-0.44
35VittozziLisaITA
532.417.0-0.710.31-0.84-0.43
36ZdoucDunjaAUT
622.717.3-0.15-0.98-0.26-0.41
37SchwaigerJuliaAUT
434.011.0-0.60-0.200.15-0.39
38PidhrushnaOlenaUKR
442.56.8-0.50-0.510.53-0.38
39TandrevoldIngrid LandmarkNOR
535.011.0-0.730.090.23-0.38
40BankesMeganCAN
351.75.3-0.17-0.28-1.56-0.37
41AvvakumovaEkaterinaKOR
531.011.4-0.28-0.63-0.05-0.36
42ZukKamilaPOL
341.30.7-0.77-0.041.08-0.34
43InnerhoferKatharinaAUT
357.00.7-1.130.890.66-0.33
44TalihaermJohannaEST
445.53.5-0.30-0.981.25-0.31
45GasparinElisaSUI
442.34.0-0.430.11-0.43-0.27
46KazakevichIrinaRUS
457.51.0-0.820.740.07-0.26
47ChevalierChloeFRA
445.57.3-0.750.270.93-0.25
48JislovaJessicaCZE
353.00.3-0.27-0.510.63-0.23
49GasparinAitaSUI
442.85.8-0.01-0.36-0.97-0.23
50BrorssonMonaSWE
538.015.00.18-0.75-0.77-0.20

Posted in Statistical analysis | Tagged World Championships

Predicting the World Championships in Pokljuka

Posted on 2021-02-06 | by biathlonanalytics | Leave a Comment on Predicting the World Championships in Pokljuka

One of the reasons I really love biathlon is that it is unpredictable. Yes, there are favourites who win more regularly than others, but there are always a large number of athletes who can take the win. Yet my next Tableau Dashboard is called Pokljuka Predictions. Well, I ran out of space to add “well, not really”. But to make predictions to the best of one’s ability, having all the right information in front of you is the best option you have. And that is what this dashboard is supposed to do: provide useful information that allows making the best possible prediction.

The dashboard works per Race Category (gender) and Race Discpline. After selecting these two parameters we can have a look at the charts but first, let’s look at some info on the events.

They are held in Slovenia, at the Pokljuka Biathlon Stadium

The program is a busy one for the athletes, but this report excludes the relays.

After setting the filters, three of the four “columns” show data, where the central column at the bottom shows data when an athlete is selected.

The scatterplot shows the athletes related to their last race in Pokljuka in this Discipline and their current standing in the World Cup for this discipline. X’s mean that the athlete either did not participate in the most recent Pokljuka event, or is not in the current season Discipline Standings. It gives an idea of what athletes are good this season and did well in the last race in Pokljuka for this discipline. They will show up in the top right.

The following two charts show similar data from above but individually: the race results for this discipline in previous seasons (when available), and the current World Cup standings for this discipline with points and ranking in brackets.

When selecting an athlete in any of these charts, we can see the current season results in the selected discipline for the selected athlete, compared to their career (since the 16-17 season) average (dark blue dashed line) and the current season’s average (light blue dotted line).

This is followed by the athlete’s current form based on consistency;it shows the absolute change in rankings per season, so the higher the value (lower on the chart), the larger the inconsistency. And the steepness of the decline shows the athlete’s form. Steep points to a big change in results, where a almost flat decline indicates that recent results were similar. It does not indicate however if these results were high or low in the ranking. If an athletes was 45th, 43rd and 44th the line will be almost flat indicating strong consistency. this will gie you an idea of likelyhood that it will change soon, or if this athlete’s performance is pretty reliable.

Last thing to mention is that when you hover your mouse over a name of an athlete, it highlights that athlete in other charts.

So all in all not a true predictor, but a tool providing information to make a better-informed prediction quicker.

UPDATE – Predictions after all, and some updates to the dashboard

So, based on the information on the dashboard I really felt I should make some predictions after all. Although I will not commit to pointing who will get what place, I will highlight the top favourites for every race, based on the dashboard.

I also changed one chart on the dashboard replacing the one that showed the current standing to the cumulative points this season to give a better idea of when the points were scored; recently or early in the season:

Men’s Sprint – It’s hard not to bet on JTB here; He won in Pokljuka in the 18-19 season, he won the last two sprints, has a lowest ranking of 4th this season and leads the Spring Standings. His brother Tarjei, was 4th two seasons ago, had a win and second place this season but a lowest ranking of 15th, so definitely more inconsistent. He’s 4th in the current standings. Outside favourites are Lukas Hofer, recently in good shape and improving, and despite his miserable rankings this season so far I would not write off Loginov: he was 3rd in 18-19 at this venue, and won last year’s World Championships in Antholz, showing he knows how to peak for a major championship. Dale and Laegreid are 2nd and 3rd in the current season standings but with only one and zero respectively World Championships and Pokljuka races under their belt (with a 23rd place for Dale in Antholz) I can’t see them ending up with a gold medal.

Women’s Sprint – Eckhoff won the last three Sprints of this season, but did not participate in the most recent Sprint in Pokljuka. She also leads the standings this season. Wierer was 2nd in 18-19 in Pokljuka, and is 10th in the current standings. She has been inconsistent this season but her last race was a 2nd place. I would consider Preuss an outsider, being 9th in Pokljuka in 18-19 and a current 4th spot in the standings. Braisaz-Bouchet is another outsider to keep an eye on, being 3rd in 18-19 and scoring a 14th, 9th and 4th position in the last three races. Hauser was 50th in 18-19, but was 3rd in the last two races this season, and has since won a 1st place so she is in very good shape.

The Men’s Pursuit is harder to predict with the obvious dependency on the Sprint results. Laegreid, JTB, and Dale lead the standings, and JTB also won in 18-19 in Pokljuka. Tarjei Boe has been improving since the start of this season (12-7-7-3) and was 6th in 18-19. QFM was 2nd in 18-19, has won a race this season, but has been very inconsistent. Loginov is an outsider again, 3rd in 18-19 but the highest position of 17th this season, as is Hofer with an 8th position in 18-19 and a 5th spot in the last race.

Women’s Pursuit – Wierer and Roeiseland, 6th and 2nd in the current standings and 2nd and 5th in 18-19 have a good chance for a top 5, but based on the current season results Eckhoff again has the best cards, especially if she does well in the Sprint. Hauser and Preuss are strong outsiders again with solid scores in 18-19 and decent results this season.

Women’s Individual – Dzhima, 2nd in the standings and winning in Pokljuka in 18-19 and a 14th in 19-20 and a 2nd spot in the last race this season has the best cards for this race. Hanna Oeberg has a strong history in Pokljuka (8th in 18-19, 2nd in 19-20) but this season has not been great for her. Hauser was 9th and 7th in Pokljuka and won the most recent race this season for her first World Cup win. Herrmann and Vitozzi are strong outsiders with solid results in 19-20 (1st and 4th) and Vitozzi in 18-19 as well (6th). Vitozzi’s season has had a tough current season with a highest position of 13th, and Herrmand aslo had a rough season so far.

Men’s Individual – Since JTB won last year in Pokljuka and a 7th spot in the year prior and 4th spot on the standings, it’s hard not see him as a favourite again. Fillon Maillet was 7th last year and has a 4th and 3rd this season so he’s a contender for sure. Loginov won the most recent race but with a 16th and 29th rank in the last two races in Pokljuka, he’s an outsider. Hofer and Laegreid are outsiders as well with a 1st and 2nd for Laegreid this season, and a 4th in the most recent race for Hofer, plus a 13th position last year.

Women’s Mass Start – Simon is hard to ignore for a favourite, winning the last two races this season and being 11th in last year’s race in Pokljuka. However, Hanna Oeberg won there last year and was 3rd and 2nd this season, so I would consider her to be the favorite, even over Simon. Outsider Roeiseland won the other Mass Start this season, and had two 7th places, as well as a 6th last year. Hauser was 3rd in the last race this season and 7th last year in Pokljuka so she is a strong outsider too.

Men’s Mass Start – This will be a tight one, and both Boe brothers and Fillon Maillet all being strong contenders, with Tarjei leading the standings and being 9th last year, JT winning the most recent race, being 2nd in the standings as well as last year, and QFM winning last year, 2nd in the most recent race and 4th in the standings. Eder is a strong outsider (8th last year and most recently, and 5th in the standings), as are Doll (2nd last year, a 4th and 7th this year), Peiffer (13th last year, a 1st, 11th and 5th this season) and Hofer (10th last year, and a 4th this season).

That’s it, those are the big players in these championships, but since we’re talking biatlon here, the chances of being wrong with predictions are pretty high.

Posted in Long-term trends, Statistical analysis | Tagged Pokljuka, World Championships

A Sturla Holm Lægreid special

Posted on 2021-02-04 | by biathlonanalytics | Leave a Comment on A Sturla Holm Lægreid special

Sturla Holm Lægreid has had an amazing season so far. This visual shows all his results in IBU Cup and World Cup races, tracked by the IBU.

First, we look at the ranks and bibs he has achieved, followed by Z-Scores for Skiing, Shooting and Range Time. Then we dial in on the shooting, looking at every shot he fired, and lastly at the moving averages of his Prone, Standing and Total Shooting Percentages.

SHLspecial (PDF) Download
SHLspecial (JPG)Download
Posted in Statistical analysis | Tagged Athlete special, Sturla Holm Lægreid

IBU -vs- WorldCup Performance, part I

Posted on 2021-02-01 | by biathlonanalytics | Leave a Comment on IBU -vs- WorldCup Performance, part I

Now that the Realbiathlon website has some IBU data as well (available to Patreon supporters), I wanted to do a comparison of metrics for athletes that competed at both levels for a minimum of five races (leaving 437 observations). Although there are different race disciplines for the two levels, in this first look I included all disciplines and combined the categories (genders). I looked at all athletes that competed in the current or last season at the IBU level, and at the World Cup level since the 16-17 season (661 athletes).

These charts are nothing fancy, just comparing athlete’s average metrics for the two levels and drawing a trend line to see what relationship exists between the results for the following metrics:

  • Total Shooting Percentage
  • Prone Shooting Percentage
  • Stand Shooting Percentage
  • Course Time
  • Shooting Time
  • Range Time

As the P-value for all charts is smaller than 0.0001 we can say all relationships are statistically significant, or, that there is a relationship between the results at the two different levels. But how strong is the relationship?

  • R2 = 0.929
  • P < 0.0001

For the Total Shooting percentage the relationship is very strong: if a shooter has a certain percentage at the IBU level, almost 93% of the time the shooter is has a similar percentage at the World Cup level.

Prone and Stand don’t differ much, with Prone having a 0.907 R2, and Stand a 0.916 R2.

Since Course time, Shooting time and Range time are not expressed in percentages but in Z-values, which looks at how how much the times differ from the average (negative is faster, positive is slower), we see different patterns. But the relationships are still very strong:

  • R2 = 0.851
  • P < 0.0001
  • R2 = 0.902
  • P < 0.0001
  • R2 = 0.899
  • P < 0.0001

In general, we can say that metrics from IBU level races translate very well to World Cup level races, but since the level of competition at the World Cup level would likely be higher than on the IBU level, the results for the athletes could, and likely will be very different. To make this more clear, here are the averages for the used shooting metrics at the two levels for all 661 athletes (rather than the subset of 437 athletes that have a minimum of five races at both levels):

  • Shooting Percentage: 80.1% (WC) versus 77.5% (IBU)
  • Prone 82.8% (WC) versus 80% (IBU)
  • Stand 77.34% (WC) versus 75% (IBU)

Note: Course Time, Shooting Time and Range Time are all Z-scores so on average they are the average (0).

From the shooting averages, we can conclude that the average shooting is better at the World Cup level than at the IBU level, which is what you would expect. So an athlete who had the same score on both levels can be above average at the IBU level and below the average at the World Cup level.

I hope to do further research on the IBU data once more seasons become available in the summer. To be continued.

Posted in Long-term trends, Statistical analysis | Tagged IBU, World Cup

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