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The Path to GOAT Status

Posted on 2023-03-25 | by Dan Covaciu | Leave a Comment on The Path to GOAT Status

Guest Essay by Dan Covaciu

How Johannes Thingnes Boe can claim the status of the Greatest Biathlete of All Time?

The current layout of the Biathlon GOAT debate

               There are few sporting discussions that are more debated and polarizing than Greatest of All Time (GOAT) conversations. In many disciplines, like basketball with the LeBron James – Michael Jordan discussion, there are analysts, fans, and even athletes themselves presenting their position on such a topic. However, every once in a while, an athlete comes out of the pack in such a dominant fashion, with remarkable longevity that it gets sporting minds questioning their position in all-time rankings. Such a case is the Norwegian Johannes Thingnes Boe, a 29-year-old biathlete from Stryn. Throughout his 10-year senior career, the Markane IL athlete has won multiple World Cup titles, with many international, World, and Olympic Titles to complete an impressive resume. His skiing speed has been a staple of his dominance for years, while his shooting has been up-and-down, with some superb performances, surrounding other inconsistent results in the range. JT has been at the top of his sport for more than half a decade, he has imposed himself in many ways, yet he may not be the GOAT in many people’s books. That is due to the impacts that Ole Einar Bjoerndalen and Martin Fourcade have had on the sport. Before delving more into the data, the outlook of this battle has JT in 3rd currently, in my books. Consequently, we will be analysing the results and steps he still has to reach GOAT status before retiring and focusing on his family and life outside of biathlon.

First Candidate – Ole Einar Bjoerndalen

Ole Einar Bjoerndalen’s World Cup results per season

Firstly, it is time to meet the two men who are in JT’s way of being the GOAT of male biathlon. Starting with, the one and only mythical figure of biathlon, Ole Einar Bjoerndalen. Fellow Norwegian Bjoerdalen holds the record for most wins at the World Cup level, with 94, as well as the record for the most podium places at 178 rostrum appearances. His 25-year-long career had many ups and downs. However, it has changed the record books for good. He holds the record for most individual Olympic Gold medals, and most individual World Championships wins and has 6 World Cup titles to boot. Furthermore, Ole Einar also has 6-second place finishes in the World Cup Total Standings level and one 3rd place in the overall standings. Without a shadow of a doubt, the sheer numbers possessed by the mythical biathlete are the best of all time. But, numbers without context are not as telling in the discussion that we will partake in today. Still, Ole Einar still has the best longevity out of the 3 contenders. In an attempt to give some context to the raw metrics provided, let’s analyse the 10-year sample of Ole Einar’s peak to get a better understanding of how good the Norwegian was at his best.   

            

NameRacesRank (Avg)WinsWin%2nd3rdPodiumsPodium%
Ole Einar Bjoerndalen47211.99419.9533117837.7
Table 1: Ole Einar Bjoerndalen’s World Cup Career Race Statistics
NameTotal SeasonsOverall Titles2nd3rdDiscipline Titles2nd3rd
Ole Einar Bjoerndalen2666120125
Table 2: Ole Einar Bjoerndalen’s World Cup Standings Statistics
Ole Einar Bjoerndalen’s average race rank (per season)

               When analysing peaks, we will be looking at the average race ranking during the 10-year span and the best year of the athlete’s career. Furthermore, we will analyse the number of wins and percentage of victories taken in that span, as well as their importance. Consequently, in Ole Einar Bjoerndalen’s case, the average race ranking from 1999 to 2009 was an impressive 5.56. However, in an even more insane fashion, the best season in that analysis, the 2004-05 season, had an average rank of 2.1. That means that in most races that season Bjoerndalen was first or second. Out of 208 races in that span, the Norwegian won 79 and podiumed in 126. The winning percentage is at a stunning 37.98%, while the podium percentage raises to 60.57%. In his best season, Bjoerndalen won 60% of all his races, while podiuming in 75%. These statistics showcase how impressive his career was and why he has long been considered one of the greatest of the sport. One record that the Norwegian does not hold, however, is the record for most World Cup titles. This record stands at 7 titles and is currently in the possession of French superstar, Martin Fourcade.

Second Candidate – Martin Fourcade

Martin Fourcade’s World Cup results per season

               The French idol, Martin Fourcade has notched up 79 wins at the World Cup level in his 15-year career, with 145 podiums to boot. The biathlete from Ceret does hold the record number of World Cup Title wins as previously mentioned, but he also has the record for most Discipline Titles, at an astounding 26. Overall, out of 15 years in the sport, he finished on the podium of the World Cup in 9 of the seasons. And when looking at his performances at major events, it is unlikely we may see someone with better performances under the brightest of light. Martin has won 11 World Titles, with 7 other medals to boot, for a win percentage at the World Championship level of 30.6%. At the Winter Olympics, Fourcade has 4 gold and 6 podiums overall, for a winning percentage of 33.3%. Based on these statistics, Fourcade was supremely consistent, winning more World Cups and Discipline Titles than his rivals, with a higher win percentage in the major events. Furthermore, he has one of the best peaks the world has ever seen, but let’s take a look and observe where it stands compared to Ole Einar’s peak marks.

NameRacesRank (Avg)WinsWin%2nd3rdPodiumsPodium%
Ole Einar Bjoerndalen47211.99419.9533117837.7
Martin Fourcade2758.27928.7402614552.7
Table 3: Comparison between Martin Fourcade’s and Bjoerndalen’s World Cup Career Race Statistics
NameTotal SeasonsOverall Titles2nd3rdDiscipline Titles2nd3rd
Ole Einar Bjoerndalen2666120125
Martin Fourcade157112644
Table 4: Comparison between Martin Fourcade’s and Bjoerndalen’s World Cup Career Standings Statistics
Martin Fourcade’s average race rank (per season)

               The 10-year selection in the career of Martin Fourcade spans from the 2010-11 season to the end of his career in 2020. Throughout his peak, the iconic Frenchman had an average race rank of 6.07. While in his best season, he reached an almost record-breaking mark of 2.2. These statistics are close, yet not quite beating Bjoerndalen’s peak results. However, if we delve deeper into the stats, Martin raced more races throughout his 10-year peak, at 241, winning 80 of them. That gives him a winning percentage of 33.19%, meaning that he won 1 out 3 races he entered in that span. In regards to podiums in that span, Martin podiumed 145 of those races, at a rate of 60.16%. Finally, in his best season, Fourcade finished with a winning percentage of 53.8%, while finishing on the podium in 84.6% of races that year – which is not even his highest mark on that statistic of his career. Overall, while Fourcade won 7 World Cups in that span, compared to “only” the 5 won by the great Ole Einar Bjoerndalen in his selected prime, I believe that these advanced numbers show one clear trend. While, the Norwegian had the slightly better peak, in terms of consistency of results, no one could overcome the French rocket that is Martin Fourcade. So, now it is time to take a look at this generation’s answer to these two greats.

This Generation’s GOAT Candidate – Johannes Thingnes Boe

Johannes Thingnes Boe’s results per season

               Johannes Thingnes Boe currently boasts an impressive 71 career wins at the World Cup level and a total of 109 podium appearances. These figures provide Boe with some very competitive win and podium percentages at 32.7% and 50.2%, respectively. These percentages are both above what Bjoerndalen has produced throughout his career, but also leading this discussion in terms of win percentage. Still, Fourcade holds an edge in terms of podium percentage. However, these statistics look good given that Johannes seems to be in his prime. When looking at World Cup titles, the Norwegian superstar officially won his 4 Big Globe after his dominant display on home ground in Oslo last weekend. And even though these numbers are lower than what both other GOATs have produced, Johannes has finished on the podium of the World Cup standings in an astounding 8 out of 13 seasons. Furthermore, in terms of discipline titles, he has what for this discussion could be seen as a “measly” number of 9 small globes.

NameRacesRank (Avg)WinsWin%2nd3rdPodiumsPodium%
Ole Einar Bjoerndalen47211.99419.9533117837.7
Martin Fourcade2758.27928.7402614552.7
Johannes Thingnes Boe2177.97132.7211710950.2
Table 5: Comparison between Johannes Boe’s World Cup Career Race Statistics with the GOAT contenders
NameTotal SeasonsOverall Titles2nd3rdDiscipline Titles2nd3rd
Ole Einar Bjoerndalen2666120125
Martin Fourcade157112644
Johannes Thingnes Boe13422984
Table 6: Comparison between Johannes Boe’s World Cup Career Standings Statistics

               In order to provide an overall, unbiased assessment, we can also analyse a 10-year sample of JT’s career. It is important to mention that if we start in the 2013-14 season, JT was 21 years old at the time and had yet to reach his prime. However, we will adjust for that later on. In this 10-year stretch, Boe’s average race rank is 7.64, with the best mark of 1.7 (meaning he is on average in the top 2 positions) this season. When we delve deeper into his results, of the 223 races in which he has competed, JT won 74 and finished on the podium in 113 races. These turn out at a percentage of 33.18% winning rate and a 50.67% podium rate. When analysing his best season to date, the just-finished 2022-23 season, his winning percentage was 82.6%, while his podium percentages reached the peaks of 95.7%. These are outstanding, never-seen-before marks that may never be replicated and it marks 2022-23 as the best biathlon season I’ve ever witnessed at the very least. Overall, the marks over a 10-year span are slightly down on the other two GOATs, but Johannes is currently having the greatest, most dominant statistical season in this conversation. As mentioned, though, he was not in his prime at the start of this 10-year span, so we can make a correction for this.

Johannes Thingnes Boe’s average race rank (per season)

               Assuming that Johannes will remain in the later stages of his peak until the 2026 Olympic Games in Cortina, we can analyse his last 7 seasons as a sample of his “peak”. This should give us a better view of how good the Norwegian was and may continue to be while at his best. In this sample, JT’s average race rank is 5.19. Furthermore, in the last 7 seasons, he has participated in 155 races, winning 63, for a 40.64% winning rate, with 99 podiums at a 63.87% rate. If we separate these 7 seasons, the dominant Boe has better statistics than either of the other two GOATs. As a result, we can clearly tell that he belongs in this conversation but is not yet the GOAT. A similar story can be told when analysing championship-level races between the three.

NameWinsPodiumsNo. of Races
Ole Einar Bjoerndalen79 (37.98%)126 (60.57%)208
Martin Fourcade80 (33.19%)145 (60.16%)241
Johannes Thingnes Boe63 (40.64%)99 (63.87%)155
Table 7: Comparing the Primes of the 3 GOAT contenders

Championship Level Races

               A great is made in the moments when the pressure to deliver is at its highest. Much like the old adage – Diamonds are made under pressure – an athlete’s complete profile (both physiologically, both also psychologically) is seen at Championship events. We will focus on the statistics of the three when it comes to World Championships and Olympic level events.

NameRacesRank (Avg)WinsWin%2nd3rdPodiumsPodium%
Ole Einar Bjoerndalen1910.8526.331947.4
Martin Fourcade129.2433.320650.0
Johannes Thingnes Boe1215.3325.001433.3
Table 8: Olympic Games Results of the GOAT Candidates
NameRacesRank (Avg)WinsWin%2nd3rdPodiumsPodium%
Ole Einar Bjoerndalen699.41115.9692637.7
Martin Fourcade367.11130.6431850.0
Johannes Thingnes Boe284.8725621553.6
Table 9: World Championship Results of the GOAT Candidates
NameRacesRank (Avg)WinsWin%2nd3rdPodiumsPodium%
Ole Einar Bjoerndalen889.71618.29103539.8
Martin Fourcade487.61531.3632450.0
Johannes Thingnes Boe407.91025631947.5
Table 10: Total Championship Level Results of the GOAT Candidates

               Based on these statistics, we can notice that Martin Fourcade has been the most consistent top-level performer among the three. Still, we need to take Ole Einar Bjoerndalen’s statistics with a pinch of salt due to his incredible longevity that resulted in some lesser performances in his later Championship presences. On the other hand, we can see that JT Boe is coming along nicely in terms of stats, and especially at World Championship level seems to consistently fight for top 5s. However, he still misses a bit in comparison with the other two. I believe that with this, we can finally rank the top performers of all time, albeit this is the part in which controversy may start to present its head.

Current Ranking of the GOATs

               While, this is an extraordinarily difficult ranking to make and I believe the best solution would be a tie, for clarity (and a bit of controversy), there should be one current GOAT. While at Championship level events Martin Fourcade holds a clear edge over the two, and especially the direct competitor (for now), Ole Einar, it is difficult to look past the sheer numbers Bjoerndalen possesses. In terms of total wins, number of Championship medals, number of podiums at World Cup level, as well number of discipline title podiums, Bjoerndalen has an edge based on his longevity. Combined with a phenomenal peak that slightly outshines Martin’s, I believe that by the narrowest of margins, the current, rightful owner of the GOAT moniker is Ole Einar Bjoerndalen. Even so, I believe that people will have different views on this and if Martin is the GOAT in your opinion, I would not put too much of a fight to argue. This does however, give us clarity as to what the template for Johannes is to take over this debate. Consequently, after all this analysis, let me present you my criteria for JT Boe becoming the GOAT of biathlon.

The Criteria to Become the Undisputed GOAT

               Firstly, in regard to sheer numbers, Johannes is not yet at the level of Ole Einar, and Martin. Starting with the World Cup Titles, JT officially earned his 4th Crystal Globe. To achieve GOAT status, I believe that 6 would be sufficient and that would entail winning two of the next 3 seasons we earlier assumed as his prime. In terms of discipline titles, the Norwegian won the Sprint and Pursuit classifications this year. This notched his tally up to 9 individual, smaller Crystal Globes. I believe this is the trickiest category to give an assessment of what he would have to achieve as it is of varying importance to every fan, and journalist. Still, if he finishes his career with more than 15 titles, with another 12-16 2nd and 3rd places in those classifications, it would meet my criteria. While these are quite high standards, based on this season, JT could have won all discipline titles, had it not been for a bout with COVID that kept him out of the Oestersund round of the World Cup. As such, the extraordinary Norwegian has taught us throughout the season that nothing is impossible for him. However, there is more to it than just raw titles won.

Fourcade vs. Boe World Cup race ranks

               Secondly, when analysing winning World Cup races, I consider that if he wins more than 85 races with a winning percentage higher than 30% that would meet my criteria. Furthermore, if he overtakes Fourcade’s podium number of 145 while increasing his podium percentage over the value of 50%, that would more than merit GOAT status. Looking at the big events, starting with the World Championships, my criteria would be that he joins the GOAT group with 11 golds, at least, while increasing his winning percentage in the region of 28-30%, from his current 25% mark. In terms of podiums, I would like to see him with more than 20 medals overall, while maintaining his incredible podium rate (currently 53.6%) above 50%. Finally, in terms of the Olympic Games, JT would have to match Ole Einar’s 5 individual gold medals tally, while overtaking Martin Fourcade’s 6 podiums (he currently has 3 individual golds, with 4 podiums). Furthermore, I would like to see him increase his win and podium percentages from the current marks of 25% and 33% up to around 30% and 40-45%, respectively. If the Norwegian were to achieve these admittedly high criteria, in the next 3 seasons, assuming his peak ends with the end of this Olympic cycle, that would also give him one of the best 10-year primes, if not the best in history. Consequently, he would take the lead in the GOAT discussion in my books.

               Most importantly, we can already say that Johannes is a generational talent who has had an incredible career. He is the leader of the post-Fourcade era of biathlon. Only time will tell whether he will be able to surpass the former faces of biathlon in Martin Fourcade and Ole Einar Bjoerndalen. However, I have raised the bar and presented my criteria and I will be enjoying watching whether Johannes Thingnes Boe can match it or better it.

Posted in Long-term trends, Statistical analysis | Tagged Data, 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

New biathlon point system

Posted on 2022-12-04 | by real biathlon | Leave a Comment on New biathlon point system

The International Biathlon Union (IBU) introduced a new scoring system for the Biathlon World Cup from this winter onwards: world championships will no longer be included in the World Cup score, no more dropped results and a major adjustment in the points system to increase the value between top results.

It’s arguably the biggest season-to-season change in the history of the sport and not everyone is happy with it.

Old vs. new biathlon point system

Rank Scoring system from
2008–09 to 2021–22
New scoring system from
2022–23
1 60 90
2 54 75
3 48 60
4 43 50
5 40 45
6 38 40
7-40 unchanged unchanged
(mostly) 2 dropped scores no dropped scores
WCH races count WCH no longer count

The IBU points system has always been an outlier compared to pretty much any other scoring system in sports, especially other FIS winter sports, because it greatly undervalued top results. Some people are concerned seasons will be decided too early now, others don’t like the fact that consistency is no longer as important. The fact that no results can be dropped any more has also been criticized by some athletes.

The new biathlon points system is still less extreme than the FIS scoring system or Formula One for example. Interestingly enough, the IBU prize money distribution has always been more top heavy than their scoring system. Let’s take a closer look at how previous seasons would have turned out with the new system.

For last season’s Overall World Cup, the new point system would have had very little effect. The top 3 for both men and women would be unchanged if you apply the rules of the new scoring system. The only World Cup score that would have been flipped is the women’s Mass Start score, which was won by Justine Braisaz-Bouchet, but now would go to Elvira Öberg with the new points system.

Both big crystal globes were won rather decisively, so it is no surprise a different scoring system wouldn’t change the outcome. For last season, there wouldn’t have been much difference in when the title race was over either. Both winners would have been crowned just one race earlier (Quentin Fillon Maillet would have clinched the title in the Otepää sprint, instead of the mass start, Marte Olsbu Røiseland would have won the title three instead of two races before the end of the season).

Things get more interesting for 2019–20. Here both the men’s and the women’s overall winner comes out different. It also gets quite complicated, because aside from the mere points, there’s also dropped results and the difference in world champion races to account for.

For the men, the season actually ended like this: Johannes Thingnes Bø 913, Martin Fourcade 911. With the new system Fourcade would have won 1019 vs. 1001. However, if you still count the world championship results, the outcome flips again, and Bø comes out on top (1286 vs. 1014).

It gets even more extreme on the women’s side. The actual score was very close: Dorothea Wierer 793, Tiril Eckhoff 786. However, using this winter’s scoring system, Eckhoff would have won the title quite easily (956 vs. 737). Mostly because of Wierer’s very strong and Eckhoff’s horrible 2020 WCHs in Antholz; results which would now no longer be included. If you count the championship races, Eckhoff still comes out on top (1039 vs. 1028), but only by 11 points, thanks to her 7 wins that season compared to Wierer’s 4.

Since 2011, five (out of 24) Overall World Cup decisions would have been changed due to the new scoring system (2011: Bø vs. Svendsen, 2014 Berger vs. Mäkäräinen, 2018 Mäkäräinen vs. Kuzmina, plus both winners in 2020 as mentioned above).

It seems that even with the new scoring system, World Cup seasons that were close before will still be close even with the bigger point spread. And for seasons with runaway winners, which we had several on the men’s side during the last decade, the point system doesn’t matter all that much. The biggest change is probably the fact that from now on wins and podiums will be much more important that consistent top 10 results.

Posted in Biathlon News, Statistical analysis

Historic biathlon results create expectations. But what about points?

Posted on 2022-06-15 | by biathlonanalytics

Introduction

The first article on the concept of the Win Expectancy Index based on Statistical Exploration, the introduction of W.E.I.S.E., explained the process and concept of win expectation. Using historic race results by athletes with variables identical to those of the athlete being analyzed, it calculated the percentage of wins. This gave us an idea of how likely it was for our athlete to win as well.

The second article on the topic gave you some examples of the possible use cases of the WEI.

After further developing the WEISE, this third article explains and demonstrates a new calculation using the same data and logic as the WEI. In this case, however, it does not look at the results binarily (winning or not winning), but rather at every result that awarded athletes with points*. The results of this calculation are summarized in the Expected Points Index (EXPI).

* see yellow box further down in the article

As a quick reminder, this is what the Win Expectancy Index looks like for all third laps of women’s sprint races (see the interactive version of the full WEI on Tableau Public):

Win Expectancy Index example
A female (W) athlete entering lap three of a SPrint race with 1 miss and ranked #3 in ski time, has a 20% chance of winning the race.

Version 2 of W.E.I.S.E.

One of the limitations of the Win Expectancy is that it only looks at the race results binarily: you either win a race or you do not win the race. But since World Cup races are rewarded with points we can use the same approach to calculate the EXpected Points (EXP).

This is a good time to remind you that W.E.I.S.E. uses calculated points based on calculated ranks, which can significantly differ from the ranks (and points) you find on the IBU website. All pursuit race results are (re)ranked based on the isolated times, thus ignoring the time differences at the start of the races which are based on the sprint race results. Also, in the season point totals, in addition to including points for Olympic Races, I do not deduct the points of the worst two races of the season, as is common in the IBU total standings. Lastly, all points are recalculated based on the current rules on awarding points as defined by the IBU.

EXpected Points

For all race participants in the last 22 seasons that were in the same race situation for cumulative misses and ski rank, we calculate the average of the points those athletes were awarded (link to interactive dashboard):

The Expected Points for lap three of Women’s Mass Start races, based on cumulative misses (top to bottom) and ski rank (left to right)

Again, like with the Win Expectancy, we can compare expected results with actual results. But now we can do so with far greater detail. Rather than saying “Eder had a 32% chance of winning after the second lap, but he didn’t win” we can now say “Eder could expecting 15 points after lap two, but got 18 points in the end”. And since we have this more detailed information at the points level, it allows us to calculate overperformance.

Overperformance

We simply subtract the EXpected Points on any lap during a race from the Actual Points (AP) at the end of that race. This measure tells us if the performance was above or below the expectation for every athlete. Overperformance is therefore defined as the actual points above the expected points. A negative value for overperformance simply indicates that the actual points were lower than the expected points. This can also be referred to as underperformance.

Let’s look at an example. In the image below, we have all races of the 2021-22 season for Julia Simon. Based on the overperformance calculation, we can see that she generally performed quite a lot better than expected:

Julia Simon has performed better than expected based on her last laps in all races in the 2021-22 season

When we turn this chart by 90 degrees and add lines for actual points (AP, green) and expected points (EXP, blue), it shows us Julia’s seasonal trend:

Again Julia Simon, comparing Actual Points (AP) to Expected Points (EXP) per final lap of the race
for the 2021-22 season (lines), and the overperformance (bars)

Running total of overperformance points

Now, rather than looking at the data race by race, let’s look at the overperformance cumulatively. Showing the running total of these values, we get a sense of how much Julia has overperformed this whole season. An amazing 120 points more than she could have expected based on historic results:

Same as above but with a running total of Over-and Under Performance based on final laps

Let’s move forward with that idea of the running total of overperformance points for the season. The following chart plots all female athletes of the 2021-22 season based on the total actual points (vertical axis) and the running total of overperformance points (horizontal axis):

Total actual points and running total of over-and under performance points for women’s final laps of races in the 2021-22 season

Based on this chart, Julia Simon, Dorothea Wierer and Lisa Hauser had the best total of overperformance points this season. They scored far more points than expected from historic results, based on their misses and ski rank going into the final lap of the races. Alina Stremous, Mari Eder and Vanessa Voigt were the worst overperforming athletes (aka underperformers) scoring much fewer points than expected.

Analyzing the past season, the athletes who overperformed did very well. Alternatively, when looking at the season ahead, athletes that underperformed last season could have expected to do better. So with some specific improvements over the summer, and perhaps some better luck, they may be able to make huge jumps next season if they can perform up to or above their expected points.

Season(s) averages and spread of overperformance

Per athlete, we can take the average of all the overperformances to see how they performed per season or over a specific timeframe. The other thing to look at is how large the difference was between their best overperformance and worst overperformance during that timeframe, the overperformance spread, or variance.

The last charts of this article shown below, also available interactively in the W.E.I.S.E. version 2 dashboard, show all athletes of the 2021-22 season plotted based on their average points overperformed per race (final laps only), and their overperformance spread:

Dorothea Wierer had an average of 4.56 point overperformance and an overperformance spread of 31.1

With overperformance it is fairly straightforward to determine good and bad: a positive overperformance is good (and the larger the better) and a negative one is not good, or at least leaves room for improvement. With the spread, it is a little less clear. As it is strictly based on the best and the worst overperformances during the selected season, it could be used, cautiously, as an indicator of consistency.

Another example. When we dig into Wierer’s data a little deeper, we can see that with an already strong average of +4.56 points of overperformance. Her average would be even better if the two negative outliers could be excluded. Did she perhaps brake a pole in the last lap of that individual race or had a fall in the last lap?

It will be up to Wierer and her team to figure out what happened in those two races to learn and improve. And for other athletes to find out how Wierer is so good at outperforming the expected points.

Dorothea Wierer had her best overperformance at 14.9 points above expected, and a worst of –16.2 overperformance, for a spread of 31.1

Conclusion and further development

In the first article on the Win Expectancy Index based on Statistical Information, I mostly focused on explaining the underlying data, processes and models, and the concept for the index itself. The second article gave some practical examples of the application of the index. This third article introduced an alternate and more detailed measurement based on the same concept: EXpected Points.

Tools like the W.E.I.S.E. are not suggested to eventually replace current and conventional wisdom, skill, expertise and knowledge in the field of biathlon. But it provides a different view based on actual data. And this is an approach not commonly and seriously used by many athletes and nations in the world of biathlon. Yet? Hopefully, articles like these will open some eyes to new possibilities that eventually, combined with, rather than instead of, current knowledge and expertise, will push biathlon athletes even further.

Thoughts or comments? You can find me on Twitter, or email me on the podcast Gmail account: PenaltyLoopPodcast

Posted in Statistical analysis | Tagged Expected points, W.E.I.S.E.

What do you expect? Practical applications of the W.E.I.S.E.

Posted on 2022-06-14 | by biathlonanalytics

Introduction

In my last article, the introduction of W.E.I.S.E., I introduced the process for creating the calculation of the expectation of winning, based on historic results with identical combinations of discipline, gender, race lap, the cumulative number of misses and cumulative ski time rank. After further developing the WEISE, this article demonstrates some practical uses of this Win Expectancy Index (WEI).

As a quick reminder, the interactive WEI dashboard can be found here and looks like this (with lap 3 selected):

Win Expectancy Index example
A female (W) athlete entering lap three of a SPrint race with 1 miss and ranked #3 in ski time, has a 20% chance of winning the race.

Examples of practical uses of the Win Expectancy Index

A tool like the Win Expectancy Index only makes sense if there is actual value in using it. Personally, I think it is very interesting to just look at the index, but I can imagine there are not too many people who share that passion for biathlon, data and data visualization. Below are some practical examples of how the Win Expectancy Index can be used to analyze athletes and races.

Athletes

Final lap Win Expectancy versus actual result

The conversion rate of opportunity to wins. For example, Elvira Oeberg’s conversion rate for races in which her Win Expectancy on the last lap was 33% or higher, was 60% (she won 3 of the 5 races).

Elvira race results in the 2021-22 season and Win Expectancy in the last lap of the race

Seasonal trend of average Win Expectancy per race for the final lap

This allows us to look at an athlete’s performance without the influence of the actual race results

The trend of JT Boe’s Win Expectancy in the final lap of every race

Average Win Expectancy per lap per discipline

Provides insight into a racer’s ability to balance their races well, and still perform well right to the final lap of a race

Looking at average Win Expectancy development per lap per athlete for sprint races

In this example, we are looking at all sprint races for women in the 2021-22 season. We then average each athlete’s Win Expectancy per lap. From this, we can see that for athletes like Roeiseland, Elvira Oeberg and Hauser the Win Expectancy increases towards the end of the race. On average, Sola, Alimbekava and Nilsson’s Win Expectancy declines in the last lap.

This gives athletes and coaches a tool to further analyse data at an individual race level to see if there is an issue or potential for improvement.

Races

Win Expectancy per race

How does the Win Expectancy change over the course of the race, and how did the eventual winner develop during the race?

Win Expectancy for athletes in the Women’s Sprint during World Cup 8 of the 2021-22 season

The trend of Win Expectancy per miss as ski rank increases

Here we can see that for athletes in the top-10 for ski rank, the difference in WE between 0 (dark green) or 1 (light green) miss is fairly consistent

Win Expectancy for all athletes in lap five of all five-lap-races with either
one (light green) or zero (dark green) misses as their race rank increases

The trend of Win Expectancy per ski rank (group) as misses increase

We can see here that the WE when skiing top-5 with one miss (~43%) is lower than when skiing top-10 with 0 misses (~49%)

Win Expectancy for all athletes in lap five of all five-lap-races with a ski rank as indicated by the labels as their race misses increase

Using Win Expectancy data to indicate some of the more exciting races

For biathlon viewers and fans, we can look at races where the highest value for Win Expectation (all athletes) during the race was 20% or less (a close pack). And within that group, the number of athletes that had that highest value of WE at some point during the race. If those numbers are 2 or more in sprint races or 4 or more in all other races, we then look at the final rank of the athlete(s) with the highest WE. If that was 10 or better we highlight the races with a pink star:

Based on this logic, the Women’s Sprint of World Cup 7 in the 2002-03 season would be interesting to watch (the highest WE of the race was only 14%, three athletes had that WE during the race and the highest rank of those three athletes was 9th). Or more recently, rewatch the 2019-20 World Cup 8 Women’s Pursuit (15%, 5 and #4).

Conclusion and further development

In the first article on the Win Expectancy Index based on Statistical Information, I mostly focused on explaining the underlying data, processes and models, and the concept for the index itself. This article dove into the application of the index by showing some examples of how it can provide value in analyzing the performances of athletes related to historic results with the same variables. I hope that this gives a better understanding of the concept of the WEI, and highlights its potential for performance analysis and athlete improvement based on a different, data-driven, view of biathlon performance.

Thoughts or comments? You can find me on Twitter, or email me on the podcast Gmail account: PenaltyLoopPodcast

Posted in Statistical analysis | Tagged Expected points, W.E.I.S.E.

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