real biathlon
    • Athletes
    • Teams
    • Races
    • Seasons
    • Scores
    • Records
    • Blog(current)
    • More
      Patreon Content Course Profiles Explanations Shortcuts
      Error Report
      Privacy Policy About
    •     
  • Forum
  • Patreon
  • Twitter
  • YouTube
    Instagram
    Facebook

Recent Articles

  • Most improved athletes this winter
  • New biathlon point system
  • Historic biathlon results create expectations. But what about points?
  • What do you expect? Practical applications of the W.E.I.S.E.
  • Introducing W. E. I. S. E: the Win Expectancy Index based on Statistical Exploration, version 1

Categories

  • Biathlon Media
  • Biathlon News
  • Long-term trends
  • Statistical analysis
  • Website updates

Archives

  • 2022
    • December
    • June
    • May
    • March
    • February
    • January
  • 2021
    • December
    • November
    • September
    • July
    • June
    • May
    • April
    • March
    • February
    • January
  • 2020
    • December
    • November
    • August
    • June
    • March
  • 2015
    • December
  • 2013
    • August
    • July
  • 2012
    • July

Search Articles

Recent Tweets

Tweets by realbiathlon

Category: Statistical analysis

Why you should check out Real Biathlon

Posted on 2021-06-07 | by biathlonanalytics | Leave a Comment on Why you should check out Real Biathlon

A few days ago @Realbiathlon posted on Twitter that his database has expanded. It now has almost 4,000 race data files for all three levels: World Cup, IBU Cup and Youth/Junior.

Why should this be important for any Biathlon lover? Well, it allows people to use it for analysis and creating visualizations to help understand things better for all three levels, and not have to start when the athletes reach the pinnacle of biathlon races in the World Cup. What did they do to get there? Were they always this good (or bad)? How long were they in the Youth/Junior level before moving to the IBU Cup and World Cup. And can we learn from what we know to predict future starts from how they perform at the lower levels? That is all in the data now available from RealBiathlon.com.

Yes, I write on his blog and yes, I am a data nerd. And although there is no denying the latter, don’t let the former fool you. I don’t get paid to write on his blog, I do it because I appreciate what he does. And hey, it gives me some exposure. Other than that, no catch. So what I write in this article is just because his work simply is very good, great for the biathlon community, and worth getting a subscription for.

And if you are curious about some of the things you can learn from the data, I encourage you to check out a previous post I wrote about IBU vs World cup data, and a dashboard I created with his data from all three levels starting in 2000-2001. It shows for example how Tiril Eckhoff has gone through the ranks and continuously improved her ski speed in relation to the top 3 in ski speed (per race and per season):

Same for JT Boe:

It also allows us to look at Ondrej Moravec’s career:

All this only scratches the surface of what is possible. So I encourage you to check out RealBiathlon.com and see what you can come up with! (and it has some pretty cool blog posts too! ;o))

Posted in Statistical analysis | Tagged Data

Is analytical comparison on your radar?

Posted on 2021-06-07 | by biathlonanalytics | Leave a Comment on Is analytical comparison on your radar?

Let’s just assume two athletes were really close in World Cup points with one more race to go. Wouldn’t it be nice to compare different statistics between the two of them? Radar charts are meant for just that particular situation! Every axis leg on a radar chart represents one statistic. And the position on that axis leg represents one of the athletes. You then connect the dots per athlete and boom! you have a Radar chart. Now if we add a line representing the Field Average and assume the inside that line is good, and outside of that line bad, and we can do some comparisons!

Going back to our example, why don’t compare JT Boe and SH Laegreid?

We can see here that SH Laegreid is faster than JT Boe for Z scores in shooting percentage, shooting time and range time, but that JT Boe is faster than SH Laegreid in the course time. On the women’s side, Eckhoff and Wierer show a very similar view:

The beauty of these Radar charts is we can do this for all sorts of analysis, like how the men and women from a Nation do when compared to the field average. Here is Norway as an example:

Mmm, I guess those Norwegians are pretty good! Another analysis we can do is comparing statistics per season. Lisa Vitozzi had a disappointing year, but where did things go wrong? The Radar chart below shows that although she was worse in every aspect shown in this chart, particularly her shooting percentage and range time were a lot worse than previous seasons:

I hope you have a better idea of what you can analyze with Radar charts, and that the power of them is to quickly see differences in statistics between multiple athletes, groups and seasons. Why don’t you go and try it out in the interactive dashboard where all the above charts were taken from? Can you find it on my profile on Tableau Public. I hope you have fun!

RJ

Posted in Statistical analysis | Tagged Comparison, Radar charts

A follow-up on going the distance.

Posted on 2021-05-06 | by biathlonanalytics | Leave a Comment on A follow-up on going the distance.

On March 14 I wrote an article called “Is the IBU going the distance” in which I found that biathlon races typically don’t go the exact distance set for race events. The following is a follow up on that article, using GPS data from athlete Baiba Bendika (follow her on Twitter or Instagram) of her two races in Nove Mesto and two races in Oberhof. I should note that the GPS data from her Strava account (she kindly exported and send them to me) comes from her watch, not sure which brand, but the accuracy will not be perfect. But then again, it’s the best I can get when it comes to tracking the course.

First I read her data in Tableau and manually divided her GPS measurements (points) into sections like lap 1, penalty lap 2, range 2, etc.

This also allowed me to see her race in a “flat line” based on distance raced and time elapsed:

We already know how many penalty laps Baiba had, so we can take out the time spent in the penalty loops. This leaves us with the following distances:

Even if we include the penalty loops and decrease the distances by 150 per loop, we get:

  • Nove Mesto on March 6: 7,307 – 600 = 6,707 (793 m under)
  • Nove Mesto on March 12: 6,948 – 150 = 6,798 (702 m under)
  • Oberhof on January 8: 8,186 – 300 = 7,886 (386 m over)
  • Oberhof on January 14: 8,281 – 450 = 7,831 (331 m over)

Now we know that her watches’ GPS is not perfect, and that some of the penalty loop section (that overlaps the course) should be included, but even then our previous conclusion that the IBU doesn’t take the distance requirements all too serious is still correct, going both under (Nove Mesto) and over (Oberhof).

The main reason why this is important is that a lot of stats like ski speed and range time are often based on the assumption that ski distances and range sections are equal from race to race and location to location. Clearly, they are not and actual distances should be accounted for when available.

(Thank you Baiba for sharing your GPS files with me!)

Posted in Statistical analysis

It’s all for the money. Or is it?

Posted on 2021-04-19 | by biathlonanalytics | 1 Comment on It’s all for the money. Or is it?

Introduction

Recently Real Biathlon posted data about World Cup Prize Money in biathlon on his website for Patreon members. Combined with all the other data sources he provides, if you like working with data and biathlon why not look into becoming a member as well and support him in keeping the site running? For the Prize Money data, you can easily find the major all-time earners, sort by gender and year, and use the data combined with other data sources he provides.

In the post below I have tried to look at the data from the less obvious perspectives, and see if we can get some interesting stories out of it.

Data source

The data contains all income from the IBU at the World Cup level, and excludes any income made from external sources like sponsorships, bonuses from national federations, etc. the data range starts with the 2003-2004 season and it should be kept in mind that some athletes were already active before this season so that data is not included. For team races, the data is based on the assumption that prize money is split entirely and equally among the participating athletes.

The Prize Money data source is in Euros as that’s the currency used in the IBU season guides, the source of this data for most (or all) seasons.

Equality

The first thing I wanted to explore is how the IBU deals with equality. And it’s fairly simple: prize money is based on race results, world cup scores, and bibs. And it is unrelated to the gender of the athlete; in other words, the prize money is equally handed out between man and women.

The small changes are likely happening as in some races more athletes get prize money, which then adds up for the season (say three athletes don’t finish a mass-start, the prize money is not handed out). The 2014 season seems to be the only year with a bigger difference, and quite frankly I don’t know why that is. But generally, we can see the pink line for the women pretty much overlaps the men’s blue line.

How is the money divided?

If we look at how the prize money is divided, we see that the majority of athletes do not make a lot from their sport (strictly looking at prize money). Of the 561 athletes in this dataset, only 27 (4.8%) have made over a million where 241 made €10,000 or less. And 428 athletes (76%) have made €100K or less since 2003-2004. It also appears the men are a bit more top-heavy with 26% making €100,000 or more, which is only 22% for the women.

Earning pace

I was curious to see how athletes compared when looking at how much they earned in their first, second, etc. year in the World Cup. I should remind you that if athletes were already active before the 2003-2004 season, their total earnings do not include the seasons before that.

We can see a number of interesting things here, for example that Bjoerndalen didn’t make money until his 12th season (due to the above-mentioned limit in available data), Martin Fourcade made the most in total but could be caught up with by JT Boe, and Jacquelin, Dale and Laegreid are also on pace to get to Fourcades total faster or go above it. Hanna Oeberg and Denise Herrmann are on track for the women to become the biggest prize money earners, although Herrmann started later in her career with biathlon.

Another way to look at this data is average money made per race. I filtered the chart to only show those athletes that have between 30 and 150 races (to highlight the “new kids on the block”) and have a average of at least €1,500 per race:

After Laegreid’s amazing first full season, it is no surprise to see him high up in the top-left corner. Dale, Jacquelin and Ponsiluoma are also in a strong position to make good money in the upcoming years if they can keep their rates going. For the women, Hanna and Elvira Oeberg, Herrmann, Tandrevold, Simon, Vilukhina and Davidova also have great ratios up to this point in their careers.

Riding the good team train

The last thing I wanted to look at are athletes that have made good money in Team races, but not so much in individual races. In other words, who has been riding the good-team train?

The chart below shows all athletes and their total prize money for team/relay races, and the percentage of earnings for team/relay races compared to their total earnings. To highlight the “train riders” (filled in circles) I selected those athletes who made more money with team/relay events than other ways (team/relay earnings > 50%) and who made more than €40,000 on team races:

Below are the highlighted athletes with more details. We can see that an athlete like Merkushyna made 81.5% of her earnings through team/relay events for a total of €51,750. Given that she has not made much money in individual races, I think she is one of the athletes who most benefitted from being on a good team. Sophie Bailley is another great example.

This Prize Money dataset is a great addition to the site, with plenty of interesting stories to tell!

Posted in Biathlon Media, Statistical analysis | Tagged Prize Money

Canadian biathletes finding balance

Posted on 2021-04-06 | by biathlonanalytics | Leave a Comment on Canadian biathletes finding balance

With three top 10s, sixteen top 20s and twenty-five top 30s in individual events, the 2020-2021 season was the best Canadian season in the last five years. After a dip in total World cup points in the 2019-2020 season, last season continued on the five-year trend of continuous increase in total world cup points:

So what did Canada do well to get here, and where is there still room for improvement? That is what the following review of the Canadian individual performances in the 2020-2021 season will analyse! In this analysis, I will only show some examples of Canadian performances, but a supportive dashboard with all used data for this analysis can be found on my Tableau Public page. It shows all stats for the Canadian team, both men and women, and has a second tab where a Canadian athlete can be selected to show his or her specific stats.

Skiing time

The skiing is still the area where the Canadian team overall can gain the most time. In the specific example below I compare the average ski times of the Canadian athletes to the field average, the average of the top 30 athletes, and the average of the top 10 athletes:

Although we can see Canada is closing up the gap a little compared to last season, we are still behind the overall average, and far behind the top 10 athletes. But the positive news is that we made great progress in the 2020-2021 season, indicated by the steep drop towards the averages.

Shooting time

This is where Canada has the least to gain (and the most to loose) as we continue to be very strong in fast shooting, leading many of the true biathlon nations:

Shooting accuracy

In previous seasons the fast shooting times by the Canadians often lead to less desired shooting percentages, but it appears the team and coaches are starting to find a balance between shooting fast and shooting accurate, demonstrated by both increased accuracy in Prone and Standing:

Shooting speed -vs- Accuracy

This balance between shooting fast and clean is of course the golden grail of shooting in biathlon (I wrote about this in more depth on this website in December last year). The chart below shows how this balance has changed for the different team, starting in 2016-2017 (thin line) and how teams have moved from shooting more or less accurate, and faster or slower. We see that in 2018-2019 team Canada put a lot of focus on shooting faster, which worked, but at the expense of less accuracy. Now we see that we have lost some speed (but are still very fast) but have gained accuracy. Hopefully, this trend can continue upwards towards more accuracy while remaining fast.

Individuals

The second part of the evaluation is looking at the individual athletes. All Canadian athletes can be viewed on the interactive dashboard, but here I’m letting Emma Lunder be the example here. We can see that here Accuracy -vs- Speed trend is similar to the Team’s and that she is gaining accuracy while giving up a little speed. But we can also see that here range, shooting and prep time (prep = range – shooting) is still very good and well below the field’s average:

Her total percentage of shooting accuracy (T) has increased mostly based on a strong increase in her prone shooting (P) and she well above the field’s average Total shooting percentage (T avg.). Lastly her skiing speed seems to have stabilized and is on par with the field’s average. But she is also closing the gap with the Top 30 and Top 10 averages.

Conclusion

As mentioned team Canada had a great season, as did Emma Lunder. The above will hopefully give a good explanation of how to read the interactive dashboard to see both men’s and women’s team performances for individual races, and the individual dashboards for any of the Canadian athletes. If the current trends continue and with some of the younger athletes coming through the pipeline, hopefully, we can some Canadian podiums in the next season! Go Canada!

Posted in Biathlon Media, Long-term trends, Statistical analysis

Posts navigation

Older posts
Newer posts

Recent Articles

  • Most improved athletes this winter
  • New biathlon point system
  • Historic biathlon results create expectations. But what about points?
  • What do you expect? Practical applications of the W.E.I.S.E.
  • Introducing W. E. I. S. E: the Win Expectancy Index based on Statistical Exploration, version 1

Categories

  • Biathlon Media
  • Biathlon News
  • Long-term trends
  • Statistical analysis
  • Website updates

Archives by Month

  • 2022: J F M A M J J A S O N D
  • 2021: J F M A M J J A S O N D
  • 2020: J F M A M J J A S O N D
  • 2015: J F M A M J J A S O N D
  • 2013: J F M A M J J A S O N D
  • 2012: J F M A M J J A S O N D

Search Articles