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Tag: Puck Possessed

Shooting Speed

Posted on 2020-12-02 | by biathlonanalytics | Leave a Comment on Shooting Speed

An analysis of shooting speed in biathlon, using the women’s individual race in Kontiolahti as an example. The data came from the real biathlon website, here is the exact link.

To get this data in a workable format, I just copied the table, pasted it in a text editor and copied/pasted that to Google Sheets. From there I had to do some splitting and moving things around but it was still fairly easy to get a working table. The only time consuming part was manually assigning hits or misses, and for that reason I only did to for the top 30 athletes. Then I added som ecalcualtion for athlete averages, max and min shooting times, etc. Although that can be done in Tableau, I find once you start working with filters etc. in becomes unnessessarily compicated in Tableau, just much easier to calculate the fields in Google Sheets.

Just a reminder the Tableau Dashboard below is interactive and intended to be used for further exploration of data. If you open it on the Tableau Public site you can use it full screen. Enjoy!

Posted in Statistical analysis | Tagged data visualization, Puck Possessed, shooting

Time Behind Score: comparing fruit, rather than apples and oranges

Posted on 2020-11-18 | by biathlonanalytics | Leave a Comment on Time Behind Score: comparing fruit, rather than apples and oranges

As IBU ranking point systems vary over time and per level (Junior, IBUcup and Senior) and typically awarded only to the top 30 athletes per race, I created the Time Behind Score to compare performances between races in different seasons and at different levels.

The Time Behind Score is based on the idea that at every level, every athlete is trying to be the fastest and wants to avoid being the last athlete crossing the finish line. As not all historic data, nor the data for all levels include skiing and shooting details, this Score only uses the final time per race, regardless of the balance between skiing-time and shooting-results. Although this leads to a lack of depth for further analysis, it is the only way to compare between different level races from different eras, and in the end, the balance between skiing and shooting is less relevant when only interested in performance based on which athletes cross the finish first.

Calculation

For the Time Behind Score calculation, all total race times per race are converted to a 0-100 scale, where the fastest athlete gets a score of 100, the slowest athlete gets a score of 0, and all other athletes get a score based on the relative position between the fastest and slowest athlete. This also gives points based on relative times rather than a rank-score that ignores how much time difference exists between positions.

The figure below demonstrates the process of converting a race result to the Time Behind Score: the top half shows the race results of all athletes with the winner on the left and the last finisher on the right; the orange dots representing each athlete are placed depending on how many seconds they finished behind the winner (so the further to the right, the more seconds behind). Those “seconds behind the winner” are converted to a percentage between the winner and last finisher in the bottom half of the image (“Percentage time behind compared to maximum time behind”) with the winner being 0% and the last finisher 100%. The Time Behind Score is the inverse of this percentage, shown on the horizontal axis of the graph, so 100 for the winner and 0 for the last finisher:

Converting race results to Time Behind Score

When comparing race results between seasons and levels, I will be using the Time Behind Score as the measurement. I hope the above will sufficiently explain the reasoning and process to calculate these values. I understand that there are (as with any other scores) pro’s and con’s but I like the pragmatic idea of scores based on how the athlete did, compared to the rest of the field. However, any comments or feedback are appreciated!

Posted in Statistical analysis | Tagged Puck Possessed, Ranking, Score, Time Behind Score

Impact of external factors on shooting performance in biathlon

Posted on 2020-08-27 | by biathlonanalytics | Leave a Comment on Impact of external factors on shooting performance in biathlon

by
Puck Possessed

In the third issue of Puck Possessed Biathlon, I want to look at the influence of things like weather and snow conditions, as well as course information. This is all summarized in reports made available on the https://biathlonresults.com/ website as Final Results – Competition Data Summary:

From this report, I used the measurements provided, except for the measurement taken half an hour before the race, as it doesn’t seem that relevant. Also, all these measurements should be taken with a grain of salt (how accurately are they measured, it’s only on one measure location, and some “measurements” are qualitative. In addition I tried my best to find a general elevation for the biathlon stadiums using Google Earth, so that data quality is also limited. Lastly, working only with the data I have, I had to make some assumptions. I realize that a maximum climb right before the shooting range makes a course harder than when it is right after the stadium. I tried looking into course profiles, but they are surprisingly hard to get (in a useful format).

To make all this data a bit easier to work with, I created a number of categories or indexes based on similar/related measurements, rather than using all data individually:

Wind

  • Wind strength (using the maximum value of the Wind Direction/Speed row);
  • Wind direction variability (the maximum difference in degrees between the three measured wind directions;
  • Wind strength variability (difference between minimal and maximum).

Visibility

  • Weather description (qualitative) is typically the same during the race, with a few exceptions (two out of 25 at the time of writing). I grouped some values in categories as they are very similar related to visibility:
    • Clear sky & Sunny
    • Cloudy, Low-level cloud, Partly cloudy
    • Light rain, Light snow, Light snowfall and Rain
    • Heavy snow & Snow

Humidity

  • Humidity measurements.

Course

  • Total Course Length;
  • Height Difference;
  • Maximum Climb;
  • Total Climb;
  • Elevation;
  • Snow of the track.

Not included

  • Air Temperature. Even though it varies, I don’t see how this could have an impact on performance, especially since events get cancelled when the temperature drops below a value where it could impact shooting. Note that I am aware that temperature impacts the tracks, but I think that is better measured by using Snow temperature;
  • Humidity. I tried to find any correlation between humidity and shooting performance but was unable to, leading to the conclusion that humidity by itself has no impact on shooting performance. Of course humidity is related to precipitation, but that aspect is covered in the Weather section.

Now the question is how to measure shooting performance. The obvious measurement is the number of shots missed, but I don’t want to ignore shooting times. For example if athlete A has no misses but takes 30 seconds longer to shoot than athlete B who may have one miss, that still says something about shooting performance compared between athletes A and B. I also considered including range time, but I consider that to be more related to ski performance. So for this exercise I am using Shooting Times and Penalty Times (in seconds) as the latter are directly related to misses and allows for combining it with shooting speed.

Next step is indexing the different categories, starting with Wind. Let’s look first at the correlation between the different wind factors and shooting performance as described above:

This tells me that the biggest correlation (and most reliable) is the wind strength, and that both strength and direction variability are not significant:

Let’s dig a little deeper here. Although on it’s own the maximum wind speed may have the most (and only) impact, how about the combination of wind speed and speed variability and direction variability?

The following charts show there is actually a almost 70% correlation between wind strength variability and maximum strength (direction variability not at all):

So we’ll need to look at combinations of maximum wind speed and change in speed. Logically it makes sense too. Even if the wind changes direction, if the wind is not very strong it won’t have much of an impact. But variable wind speeds, especially whit some strong gusts are tough to adjust to).Now how about visibility? That becomes a bit more complicated, or less objective, as we don’t have measures for visibility, but rather subjective observations. Let’s look at the number of athletes with specific number of misses per race per season, and relate that to the weather description:

This gives me some indication of what are good shooting conditions, and which ones are less preferable. Let’s simplify this a bit more, by assuming a solid shooting performance is two misses or less; anything more and you are typically out of the race for gold (expect when you have exceptional ski speed):

Based on all this information (and knowingly ignoring other factors that contribute to these number), I’m going to state that Clear sky, Sunny, Cloudy, Light snowfall and Rain typically lead to solid shooting performances, with well over 70% of all athletes having 2 misses or less, whereas Partly cloudy, Snow, Heavy snow, Light rain, Light snow and Low-level cloud lead to lesser shooting performances. Partly cloudy, Light snow and Light rain appear to be the worst conditions.

That leaves us with the course conditions. And other than Total Climb in meters (which is still statistically insignificant with a p-value of 0.06) none of the course condition factors show any correlation to shooting performance (defined as shooting and penalty times), with p-values over 0.7 and R2-values lower than 0.005:

These charts look at event averages, but looking at individual athlete shooting performances the results are very similar:

Although it is hard to imagine course conditions having no indirect impact on shooting performance (many steep climbs, especially before entering the stadium, or wet, slow snow which makes the athletes work harder, etc.) I’m going to assume there is no direct impact on shooting performance. But that would be an interesting analysis for a future edition of Puck Possessed Biathlon for sure.

So in summary, we are going to index or score wind influence and visibility influence. And based on the information we gathered so far, I’m going to say that 

Weather

Clear sky, Sunny, Cloudy, Light snowfall and Rain = Good

Snow, Heavy snow and Low-level cloud = Medium

Partly cloudy, Light snow and Light rain = Bad

Wind

IF [WindStrengthMAX (copy)] >= 2 AND [WindStrengthDiff] >= 1.2 THEN "Bad"
ELSEIF [WindStrengthMAX (copy)] >= 2 AND [WindStrengthDiff] < 1.2 THEN "Medium"
ELSEIF [WindStrengthMAX (copy)] < 2 AND [WindStrengthDiff] >= 1.2 THEN "Medium"
ELSE "Good"
END

Now we can assign values to good, medium and bad (1, 2 and 3) and create a External Factor Index, that we can then try to measure up against the Shooting Performance indicator described earlier:

The green dots symbolize events in the 2017-2018 season, yellow 2018-2019 and grey the current season.

All in all a lot of work to come to the conclusion that there is a correlation between our defined Shooting Performance, and the External Factor Index, mostly based on wind and weather: the P-value is 0.0041 and thus significant, and the R2-value is 0.295. 

As I am sure you have figured out if you got this far, my statistical knowledge is limited. But I would say, that based on all assumptions made above, roughly 30% of shooting performance is impacted by weather conditions mentioned above.

Of course this research can use a lot of improvement. For example rather than comparing average shooting performances per event, look at standardized shooting performances. And the External Factor Index is based on a number of assumptions that are, to say the least, arbitrary. But the exercise was fun, and I believe I learned a lot more about the data of women’s biathlon sprint races.

If you have any feedback or comments, please reach out on Twitter: @rjweise

Posted in Statistical analysis | Tagged Puck Possessed, shooting

The Queen of Pursuit

Posted on 2020-08-27 | by biathlonanalytics | Leave a Comment on The Queen of Pursuit

by
Puck Possessed

I did research on Pursuit races of the 2019-2020 season to find out who the real Queen of Pursuit was for the last season. Here’s a summary of my findings:

Posted in Statistical analysis | Tagged Puck Possessed, pursuit

Canadian performances in Biathlon since 2000

Posted on 2020-06-24 | by biathlonanalytics | Leave a Comment on Canadian performances in Biathlon since 2000

By Najtrebor

This article mainly uses data from Senior-level, non-team IBU Biathlon events since 2000, unless indicated otherwise. The data come from the IBU (www.biathlonresults.com) starting with the 2000-2001 season, and going up to (and including) the 2019-2020 season). It combines all race results with Event, Race and Athlete data, although the Athlete data is limited to Senior and IBU Cup level. Unfortunately, the detailed race-data with ski- and shooting times per loop, is only available through PDF-files that so far provide a too-big-a-hurdle to conquer! (Clearly, I wrote this article before this amazing website was re-instated!) But what the current data set provides is ranks, final times and general shooting results.

Participation – all levels and events

While the average number of all participants in IBU events has been very steady in the last 20 years, Canada’s participation has slowly increased, as shown in Figure 1. Although the growth appears to be levelling out in recent years at all three levels, over the 20 year period there is a slow incline, specifically for Junior and Senior events.

Note that the 2019-2020 season was not fully completed due to COVID19, likely leading to lower overall participation numbers.

World Cup Points

The total number of World Cup Points per nation is dependent on the number of participants and how race results are linked to points. But at this level of (semi-)professional sports, to evaluate a nation’s success, the points are usually all that matters. Since the total number of points is more relevant when compared to other nations, the chart below shows national points per season at the Senior level for non-team events, for all nations, with Canada highlighted in red.

Unfortunately, it appears Canada has been in decline in the last couple of seasons.

The chart on the right split some of these nations out so see individual trends and how well nations do and how they are trending. The horizontal dotted line is the base-line (0 points) so the further above the line, the more points nations have scored. It shows that Norway, France and Germany are the main high-scoring nations, Russia has lost touch with the top, and Sweden and Italy are increasing. The other shown nations, including Canada, or relatively stable at this scale.

Canada’s best

The 2019-2020 season was in the middle of the pack of all seasons since 2000, but again note that it was cut short due to COVID19. The 2014-2015 season was our best so far, looking at World Cup points, with very strong performances by Rosanna Crawford and Nathan Smith.

The charts below looks more at the development of Canadian athletes, showing total World Cup points won in individual Senior events on the vertical axis, and the n year of their career on the horizontal axis. The left side shows all athletes to highlight Canadians in the complete picture, where the right side zooms in on Canadians.

Canada’s best seasons: team events

As the last couple of charts, figure 6 shows team events per nation, and how Canada performed since the 2000-2001 season by looking at the average Rank of all seasons (vertical axis) and the number of races started by a nation (horizontal axis).

In contrast to the figure above, the figure below shows the average rank per season as a line starting in ’00-’01 and ending in ’19-’20. As Canada starts in the bottom left (labelled 0001 for season ’00-’01) it starts moving to the right (more races) and up (better average ranking). The upward progression stops after the ’13-’14 season, but the ’19-’20 season shows an improvement again. Hopefully, this can be continued in the next season!

The full pdf of the article above can be downloaded below.

PuckPossessed7_CanadiansDownload
Posted in Statistical analysis | Tagged Canada, Puck Possessed

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

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