Long-term trends – real biathlon

The Path to GOAT Status

Posted on 2023-03-25 | by

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 3^rd 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 3^rd 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.

Name	Races	Rank (Avg)	Wins	Win%	2^nd	3^rd	Podiums	Podium%
Ole Einar Bjoerndalen	472	11.9	94	19.9	53	31	178	37.7

Table 1: Ole Einar Bjoerndalen’s World Cup Career Race Statistics

Name	Total Seasons	Overall Titles	2^nd	3^rd	Discipline Titles	2^nd	3^rd
Ole Einar Bjoerndalen	26	6	6	1	20	12	5

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.

Name	Races	Rank (Avg)	Wins	Win%	2^nd	3^rd	Podiums	Podium%
Ole Einar Bjoerndalen	472	11.9	94	19.9	53	31	178	37.7
Martin Fourcade	275	8.2	79	28.7	40	26	145	52.7

Table 3: Comparison between Martin Fourcade’s and Bjoerndalen’s World Cup Career Race Statistics

Name	Total Seasons	Overall Titles	2^nd	3^rd	Discipline Titles	2^nd	3^rd
Ole Einar Bjoerndalen	26	6	6	1	20	12	5
Martin Fourcade	15	7	1	1	26	4	4

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.

Name	Races	Rank (Avg)	Wins	Win%	2^nd	3^rd	Podiums	Podium%
Ole Einar Bjoerndalen	472	11.9	94	19.9	53	31	178	37.7
Martin Fourcade	275	8.2	79	28.7	40	26	145	52.7
Johannes Thingnes Boe	217	7.9	71	32.7	21	17	109	50.2

Table 5: Comparison between Johannes Boe’s World Cup Career Race Statistics with the GOAT contenders

Name	Total Seasons	Overall Titles	2^nd	3^rd	Discipline Titles	2^nd	3^rd
Ole Einar Bjoerndalen	26	6	6	1	20	12	5
Martin Fourcade	15	7	1	1	26	4	4
Johannes Thingnes Boe	13	4	2	2	9	8	4

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.

Name	Wins	Podiums	No. of Races
Ole Einar Bjoerndalen	79 (37.98%)	126 (60.57%)	208
Martin Fourcade	80 (33.19%)	145 (60.16%)	241
Johannes Thingnes Boe	63 (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.

Name	Races	Rank (Avg)	Wins	Win%	2^nd	3^rd	Podiums	Podium%
Ole Einar Bjoerndalen	19	10.8	5	26.3	3	1	9	47.4
Martin Fourcade	12	9.2	4	33.3	2	0	6	50.0
Johannes Thingnes Boe	12	15.3	3	25.0	0	1	4	33.3

Table 8: Olympic Games Results of the GOAT Candidates

Name	Races	Rank (Avg)	Wins	Win%	2^nd	3^rd	Podiums	Podium%
Ole Einar Bjoerndalen	69	9.4	11	15.9	6	9	26	37.7
Martin Fourcade	36	7.1	11	30.6	4	3	18	50.0
Johannes Thingnes Boe	28	4.8	7	25	6	2	15	53.6

Table 9: World Championship Results of the GOAT Candidates

Name	Races	Rank (Avg)	Wins	Win%	2^nd	3^rd	Podiums	Podium%
Ole Einar Bjoerndalen	88	9.7	16	18.2	9	10	35	39.8
Martin Fourcade	48	7.6	15	31.3	6	3	24	50.0
Johannes Thingnes Boe	40	7.9	10	25	6	3	19	47.5

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 4^th 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 2^nd and 3^rd 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.

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.

Introducing W. E. I. S. E: the Win Expectancy Index based on Statistical Exploration, version 1

Posted on 2022-05-13 | by

biathlonanalytics

Introduction

History

For many years, sports data analysts in numerous fields have used results from the past to predict outcomes of similar situations in the future with a specific level of certainty. In baseball, for example, one can foresee the outcome of an at-bat with specific parameters (2 strikes, 2 balls, 1 out, batting right, pitching left, and a score of 4-2 in the 7th inning) based on all similar situations in previous seasons of baseball. In (ice-)hockey one can do the same when looking at the shot location, the period and the current score with a specific goalie in the net, or the home-team win expectation based on a certain score and with a specific amount of time left in the game. All in all, there is a long history of using historical results to predict future ones. The article below takes a first stab at doing something similar for our beloved sport biathlon.

What is W.E.I.S.E.?

Since the end of the 2021-2022 season, I have been working on The Win Expectancy Index based on Statistical Exploration, W.E.I.S.E. or WEI for short. It uses results from previous biathlon races to predict with a level of certainty what the outcome of a race can be. Unlike baseball, and to some degree hockey, biathlon doesn’t offer its data ‘live’ during a race, but we can still use the WEI to analyze athletes’ performances while comparing predicted results and actual results, look at the strength and weaker points of an athlete during a race, and what component of the race to focus on, to name a few.

The WEI only just sprouted, and I’m already claiming quite the impact and outcome of it! Please don’t take me too seriously (but please, also don’t take me not serious at all). Although I highly value data and statistics in sports, I fully realize they are just one of the means that can be used for analyzing biathlon, and do not replace but rather enhance any other type of analysis. And outcomes are just predictions based on other events that, on paper, are the same, but in reality can be quite different, and as such, while providing value, should be taken with a few grains of salt.

Goal

My goal with the WEI and this article is to share my thoughts, describe the creation process and explain its possible applications. And by doing so I hope to get your interest and provide me with feedback. Yes, I am highly interested to hear your thoughts, know if you see any value in the WEI, and read about what thoughts you have when you read this! (fire me a tweet if you care to share!) In the end, I want to keep improving, updating and refining the WEI, and getting different perspectives will help that tremendously. And although it would sadden me after double-, triple- and quadruple checking everything, if you do find anything wrong or broken in the process described here or the index demonstrated further down, please do let me know.

Data

Basics

The basic data for this article are the individual race results on a participant level, as gathered by RealBiathlon, for all seasons starting in 2001-2002 up to and including 2021-2022. From all those races, the following results were excluded: Null (no values), DNF (did not finish), DNS (did not start), DSQ (disqualified), and LAP (lapped).

To give you an idea, we are talking about 1,068 races in total (136 individual, 200 mass starts, 328 pursuit and 402 sprint races. All these races had a combined total of 76,712 participants, 1,143,230 shots and 226,896 misses (19.8%).

Key statistics

For the first version of the WEI, I focus on two specific statistics that greatly impact the results of a biathlon race: ski speed, expressed in course time, and shooting, expressed in misses. Since I want to be able to look at win expectancy at every end of a lap (post-shooting) I calculated cumulative misses per lap and cumulative course time per lap which I then ranked.

(Re-)Calculations

I also (re-)calculated final race ranks and points, for a number of reasons:

First, I wanted to include Olympic Races for which no World Cup points are rewarded
Second, when using such a long time period, some point rewarding rules may have changed, so I wanted the points to reflect the current rules for awarding points
Third, as pursuit races can be heavily influenced by start times, I used the isolated times (actual race time ignoring start time differences) and calculated the final result rank and points.

Note that this last item can lead to odd-looking results, but please trust that the calculations are fine. For example, we know that Quentin Fillon Maillet won six pursuits in a row this season (including Olympics). However, his isolated results in those races were 5th, 2nd, 3rd, 14th, 3rd and 7th.

Data kaputt?

When looking at data for the 4th and 5th laps, some sprint races may appear to be broken. This is not the case but due to being the only discipline that only has two shootings and three laps. In the dashboard I share below I have made sure sprint races are not shown for laps four and five, but when more charts become available in the near future, you may see some oddities for laps four and five in sprint races. Now you know what causes it.

Unfortunately, some races are missing course time data for certain or all participants. This was likely due to broken time tracking equipment or ankle straps on the day of the race, or someone forgot to turn on the trackers, or something comparable. Since this data is actually ‘kaputt’, these results were removed from the dataset, as nulls can not be used in the calculations and counts (118 participants in total).

Analysis

Levels of detail

Based on the data described above, I wanted to create this first version of the WEI with cumulative misses and cumulative course time combinations, that possibly could be generalized in bins. As we are only able to look at results per lap, we don’t have the luxury of generating a huge number of historical results as they do in baseball for example. To ensure a decent sample size is available, grouping ski rankings in bins seemed to make sense. I also wanted to make sure that the resulting dashboards would allow users to look at the data as a whole, per gender and per discipline.

Calculating Win Expectancy

After organizing all the data in such a way that I had combinations of race/athlete/lap/cumulative misses/cumulative ski time rank, I could then calculate the total occurrences of these combinations, as well as the total number of race winners with those combinations.

For example, after the first lap, the most occurring combination was that of 535 participants with zero misses and ranked 13th in ski time, of whom 35 ended up winning the race. The Win Expectancy (WE) for that group would be 35/535 = 6.5%. Not surprisingly, the 501 participants without misses and ranking first in ski time after the first lap had the highest WE, with just under 30%. These WEs are still relatively low, as they are calculated after the first lap and shooting. A lot can still change, especially in the race disciplines that go five laps.

Reliability

The sprint races, only having three laps (while having the most participants), will have their most reliable WEs after lap three, when both their cumulative misses and ski time rank will no longer change. The athletes in all race disciplines with zero cumulative misses and ranked first in cumulative ski time after lap three have a 76.4% chance of winning (94 winners out of 123 athletes in total), based on historic results. If we look at athletes after lap three in sprint races only, the WE goes up to 89.6% (69 out of 77).

Sample sizes

The more specific we get with regards to race parameters, the fewer athletes and results we will have to compare to (smaller sample size). Especially when we look at mass starts, as those races already have a reduced number of athletes only starting 30 per race. For example, when we specifically look at the combination of the mass start discipline, women, one miss and ski time rank five, we will only find ten athletes (of whom two won, so the WE for that group is 20%). And as we go further down in misses and rank for that group, say for six misses and ski time rank 18, we only have four athletes.

Bins

To address this we can use bins for the ski time rank, groups of values close together. So far I used bins with a size of five, combining all athletes from the same original group above (mass start, women, one miss) in ski time rank groups of 1-5, 6-10, 11-15, and so on. The ten athletes we found with one miss and ski time rank five are now in the group with one miss and ski time ranks 1-5, giving us 68 athletes including 27 winners, for a WE of 39.7%.

The W.E.I.S.E.

Now that I have introduced and described the creation of the W.E.I.S.E., let’s just have a look at it. Below is an interactive version of the Win Expectancy Index. Based on your input in the filters for Lap, Ski Rank or bin, Gender and Discipline, it shows the Win Expectancy for any combination of cumulative misses and cumulative-ski-time rank. If you hover your mouse over a data point it will show a pop-up with additional details, as shown below:

In case the embedded version below is not working or not displaying well on your screen, please go to the same dashboard on my Tableau Public site. If you’re not sure how to use the filters and such please see my previous post with some tips and tricks on how to use Tableau dashboards.

Applications of the W.E.I.S.E.

Examples

Now that you have a better understanding of the W.E.I.S.E. and an idea of what it looks like, the question arises: how does it add value? The following are examples of how I believe the W.E.I.S.E. can be used to better understand biathlon:

See what combinations of misses and ski time rankings have the best WE for each lap
Compare combinations for WE vs number of occurrences (probability vs reliability)
Compare the WE to actual race results and see how they relate or why they differ
See if the WE declines as ski time rank increases with the same number of misses
Reversed, see if the WE declines as the number of misses decreases while the ski time rank (bin) stays the same
For each lap in a race, analyze the changes in WE per athlete
Look for commonalities when doing the above for a number of races for a specific athlete to identify potential weak spots or strengths
Aggregate the WE per lap per race and compare races
Analyze if multiple and/or big changes in WE in a race relate to interesting races to (re-)watch
See if the aggregated WE can tell us anything on a nation’s level
Once live becomes available from the IBU, it can be used to see live updates on Win Expectation for any athlete, and even (although it’s definitely not my thing) use it for betting during the race
Change statistics to some that may better represent skiing and shooting
Develop this further to show expected points per combination rather than just the expectancy to win.

Share your thoughts, please!

That’s quite a list of things that I can just come up with after giving it some thought. But let’s not stop there! If you made it through the article all the way to here, please let me know (Twitter) your thoughts about the application of the W.E.I.S.E. and your ideas on how to improve and expand it in the next version. If you’re not on Twitter, I’m also on Instagram and you can also email me (just add rj@ in front of my main website name). I would really appreciate your time and attention. I have plans to embed some of the ideas above in newer versions of W.E.I.S.E. in the following weeks, but new perspectives really help in improving it, so I’m looking forward to your feedback and the conversations!

Individual Olympic gold medals in biathlon (1960 – 2022)

Posted on 2022-02-19 | by

real biathlon | Leave a Comment

Individual/non-team Olympic titles in biathlon – updated (1960 – 2022)
Complete record list:
https://www.realbiathlon.com/record

Has the field gotten narrower?

Posted on 2022-01-21 | by

biathlonanalytics | Leave a Comment

Introduction

After listening to an episode of Doppelzimmer, a german podcast in which Erik Lesser and Arnd Peiffer talk about biathlon, I got curious. Curious about analyzing if the field has gotten narrower between biathlon nations in the last two decades. Erik and Arnd were talking about this and saying that people always mention the field is getting narrower, but that it would be interesting to do some analysis about it to see if this is actually true. This is my analysis on that topic.

Data

For this research I used all Men’s Relay races on the IBU World cup, World championships and Olympic Games since the 2000-2001 season, ending at the 5th event of the current 2021-2022 season. I removed all nations that did not start, did not finish, got lapped, etc. Then I did some conversions of times from hours, minutes and seconds into seconds and did data validation as some years had some bad data quality in some fields.

Measures

Then the question was how to measure the narrowing of the field in biathlon. I took a two-sided approach on this: for one I looked at how many seconds the 15th ranked team was behind the eventual winners of the race. This should give me a good idea of how much the weaker teams are behind the top team, expressed in time. The other approach was to see how many teams finished within 5 minutes of the winning team. This gave me another look at how many teams could be considered stronger teams.

The 15th rank and the 5 minutes are variables that can be debated forever. But the reasoning for the 15th team is that in many cases there weren’t that many more teams in the race that finished. The 5 minutes is an arbitrary number I decided on after spending some time going through the data and looking at the distance in time between the better teams of the time.

Analysis

As one would expect, due to different venues, weather conditions, team lineups and other factors, the results are kind of up and down from one race to the next.

Although we can kind of see some vague hints of a trend in the second chart, it really doesn’t show it at a level that would make me comfortable to claim a trend exists.

Luckily we can use the moving average function. For every race, this takes the race’s result plus the previous 10 races (about two seasons worth) and averages them. This gives a clearer picture and a better idea of the trends over the last two decades.

Results

The first chart, about the time behind the leaders for teams ranked 15th, shows that despite some waves going up and down, over time the time behind the leaders has slowly but steadily decreased, from about 500 seconds to between 250 and 300 seconds. That means the 15th ranked teams have gotten 3 to 4 minutes closer in the last 20 years.

The second chart, with the number of nations within 5 minutes from the winning team, shows an even more wavy pattern. Since about 12-15 seasons ago, the general number of teams has been going up, ranging between 13 and 16, but from there it seems to have stabilized.

Overall, I think the “smaller” biathlon nations are getting closer to the leaders of the pack, but the number of top nations appears to have stabilized in the last 5-10 years. What do you think? Would other ranks and seconds from the winner values be better to use for this analysis? Please let me know on Twitter or use the interactive version of this chart and see for yourself!

Olympic medals in biathlon (1960 – 2018)

Posted on 2022-01-12 | by

real biathlon | Leave a Comment

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