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Tag: projection

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

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  • New biathlon point system
  • Historic biathlon results create expectations. But what about points?
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  • Introducing W. E. I. S. E: the Win Expectancy Index based on Statistical Exploration, version 1

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