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Year: 2020

Improvements season-to-season, putting it to use

Posted on 2020-11-19 | by biathlonanalytics | 2 Comments on Improvements season-to-season, putting it to use

To highlight what a great site Real biathlon is, and how easily the data can be used to give some great insights, below is a step-by-step on how to make a quick interactive chart based on the data referenced in the previous article. I used Google Sheets and Tableau Public, but you can use any of these kinds of tools that you are comfortable with, and publish a lookup chart within an hour. That’s how easy the real biathlon site is to collect biathlon data!

Step 1 – download & store the data

For the created chart I used the data of the last five season, for both men and women, limiting the data to only those athletes with at least 10 races. Since that never resulted in more than 100 athletes per gender and season, I did not set a filter on that.

After selecting the Season Statistics, Performance Score (for example, the 2019-2020 season for women), you can just select the table, copy, open a blank Google Sheet, select cell A1 and paste. In my case, my first row showed twice so I just removed one of them. Do this 10 times (five per gender), name the sheets appropriately (W1920, W1819, M1920, M1819, etc.) and export from Google Sheets to an xlsx file. You can export to text, but you would have to do that per sheet, where exporting to excel (xlsx) exports all sheets at once.

Step 2 – open data in Tableau

In Tableau Public connect to the spreadsheet by:

  • clicking on the Data menu > New Data Source
  • click on Microsoft Excel in the Connect window
  • select the xlsx file you just exported from Google Sheets
  • at the bottom of the list that shows your sheet names, double click New Union
  • drag all 10 sheets into the Union window
  • click OK

Step 3 – create some calculated fields

  • Full Name = [Given Name] + ” ” + [Family Name]
  • Gender =
IF LEFT([Table Name],1) = "M" THEN "Men"
ELSEIF LEFT([Table Name],1) = "W" THEN "Women"
ELSE "Unknown"
END
  • Season = MID([Table Name],2,5)

Step 4 – create the chart

Depending on what you want to show in your chart, the following differs, but to replicate the chart I made, drag the following pills in the Filters, Marks, Columns and Rows:

Step 5 – publish to Tableau Public

Once you are happy with your chart, just save the file to Tableau Public.

Now users can use highlighters to see how their favourite athletes stack up against the field, or see how certain Nations fare.

Posted in Statistical analysis | Tagged data use examples, data visualization, Tableau

Improvements season-to-season

Posted on 2020-11-18 | by real biathlon | Leave a Comment on Improvements season-to-season

The new website allows you to look up basic biathlon data on your own (for different disciplines, periods, categories, etc.), so I won’t be posting too many of the regular statistical updates that I have done in the past. If you are interested in a specific statistic or ranking, you can always check out:

  • 2019–20 Shooting hit rates: Men | Women
  • 2019–20 Ski speed: Men | Women
  • 2019–20 Shooting Times: Men | Women
  • 2019–20 Range Times: Men | Women
  • 2019–20 Shooting efficiency: Men | Women
  • 2019–20 Overall Performance Score: Men | Women

These will be updated after each race. I thought it would still be interesting though to take one high-level look at last season’s performances. Below I listed the season-to-season changes in the Overall Performance Score of regular World Cup athletes (at least 14 races in the last two seasons).

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 values 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

Émilien Jacquelin was the most improved athlete last season, getting better in all major aspects of the sport: 5.5% higher hit rate, 1.8% faster skiing and 1.8s lower range time. Vytautas Strolia improved by the same amount, albeit on a much lower level, earning his first career top 20. They are followed by Johannes Kühn, who almost halved his average ski rank (12.3 to 6.7), and Erlend Bjøntegaard, who managed to increase his hit rate by 7.4%. On the flip side, Lukas Hofer‘s and Benjamin Weger‘s performance scores declined the most; both skiing over 1% slower; Hofer also hit 4.5% less of his targets.

Martin Fourcade ended his record-breaking career with his highest ever hit rate (91.8%), while his ski speed was almost back to his previous best (after a big decline in 2018–19): he had an average Course Time rank of 6.0 last winter – in 5 of his 7 title winning seasons his average ski rank was in the 4.5-5.0 range. The improvement of the French men really stand out (three in the top 10 below). Quentin Fillon Maillet became the second-fastest skier overall (1.5% faster). Johannes Thingnes Bø‘s ski speed declined slightly (on the highest possible level), yet he managed to set the best shooting percentage (92.1%) for a World Cup winner ever.

2019–20 z-Scores compared to 2018–19 | 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
1JacquelinEmilienFRA
20-1.27-0.83-1.36-1.15-0.44
2StroliaVytautas LTU
14-0.68-0.050.30-0.38-0.44
3KuehnJohannesGER
21-1.510.02-0.17-0.90-0.28
4BjoentegaardErlendNOR
18-1.38-0.77-0.40-1.08-0.28
5SeppalaTeroFIN
17-0.980.15-0.55-0.60-0.26
6FourcadeMartinFRA
21-1.63-1.26-0.71-1.41-0.21
7PrymaArtemUKR
20-0.79-0.53-0.72-0.71-0.20
8Fillon MailletQuentinFRA
21-1.68-0.84-1.05-1.36-0.15
9IlievVladimirBUL
16-1.140.45-0.44-0.60-0.12
10EliseevMatveyRUS
20-0.63-0.68-0.85-0.67-0.11
11BoeTarjeiNOR
21-1.50-0.93-0.51-1.21-0.09
12BoeJohannes ThingnesNOR
17-1.89-1.30-1.01-1.62-0.08
13GuzikGrzegorzPOL
14-0.18-0.19-0.81-0.26-0.08
14KrcmarMichalCZE
20-0.84-0.44-0.59-0.69-0.08
15BormoliniThomasITA
15-0.59-0.62-0.47-0.59-0.06
16YaliotnauRamanBLR
14-0.920.240.18-0.46-0.05
17PidruchnyiDmytroUKR
19-0.83-0.50-1.35-0.80-0.05
18DollBenediktGER
21-1.42-0.26-1.04-1.04-0.03
19ClaudeFlorentBEL
17-0.58-0.730.78-0.46-0.02
20FakJakovSLO
20-0.77-1.00-1.04-0.87-0.02
21SamuelssonSebastianSWE
16-0.94-0.22-0.44-0.67-0.01
22ChristiansenVetle SjaastadNOR
21-1.18-0.70-0.69-0.98+0.03
23NelinJesperSWE
17-1.050.00-0.28-0.66+0.03
24LoginovAlexanderRUS
19-1.22-1.03-1.15-1.15+0.03
25LeitnerFelixAUT
19-1.00-0.370.16-0.68+0.04
26BauerKlemenSLO
15-0.59-0.10-1.46-0.55+0.04
27MoravecOndrejCZE
17-0.54-1.06-0.71-0.71+0.05
28DesthieuxSimonFRA
21-1.33-0.76-0.81-1.10+0.08
29EberhardJulianAUT
18-1.350.24-1.11-0.86+0.13
30GaranichevEvgeniyRUS
16-0.80-0.86-0.64-0.80+0.16
31GuigonnatAntoninFRA
15-0.96-0.52-0.72-0.80+0.17
32RastorgujevsAndrejsLAT
17-1.160.11-0.26-0.68+0.18
33PeifferArndGER
20-1.15-0.97-0.79-1.06+0.18
34WindischDominikITA
21-0.990.13-0.04-0.55+0.22
35DohertySeanUSA
14-0.49-0.33-0.61-0.46+0.25
36EderSimonAUT
15-0.67-0.86-1.12-0.78+0.28
37WegerBenjaminSUI
16-0.85-0.34-0.19-0.62+0.33
38HoferLukasITA
20-1.160.12-0.06-0.65+0.35

Women

Tang Jialin improved the most among regular starters, skiing an impressive 2.5% faster. Emma Lunder increased her hit rate from 74.3% to 82.1% and lowered her average Course Time rank by 7.4. Baiba Bendika improved virtually by the same amount, mostly thanks to skiing 1.7% faster. Not far behind was Tiril Eckhoff, who went on an incredible run of 6 wins in 8 races, in large parts thanks to a career-best hit rate (83.1%); her already high ski speed also increased slightly, however, she had been faster in 2015–16.

One of the pre-season favorites, Lisa Vittozzi, had a winter to forget: her overall shooting percentage fell by 7.6%, while her ski speed declined roughly to its 2017–18 level. Susan Dunklee proves that aggregate data isn’t everything, winning world championship silver in one of her worst seasons statistically. Dorothea Wierer claimed her second overall title, shooting minimally worse (-0.9%), but skiing faster than ever (career-best average Course Time rank: 10.0). Kaisa Mäkäräinen ended her long World Cup career (358 individual top-level races, 3rd all time) on a slight uptick; although her hit rate stayed below 80% for a second consecutive year, she managed to improve her ski speed in her final season.

2019–20 z-Scores compared to 2018–19 | 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
1TangJialinCHN
14-0.36-0.941.15-0.35-0.49
2LunderEmmaCAN
18-0.50-0.57-1.29-0.61-0.46
3BendikaBaibaLAT
17-0.77-0.52-0.88-0.71-0.45
4EckhoffTirilNOR
20-1.57-0.67-0.40-1.17-0.42
5BraisazJustineFRA
21-1.450.09-0.18-0.85-0.35
6FialkovaIvonaSVK
16-0.800.25-0.30-0.44-0.33
7SemerenkoValentinaUKR
16-0.70-0.65-0.77-0.69-0.30
8ZukKamilaPOL
15-1.060.480.38-0.44-0.28
9SanfilippoFedericaITA
15-0.69-0.420.03-0.52-0.26
10PreussFranziskaGER
17-0.95-1.17-1.40-1.07-0.25
11DavidovaMarketaCZE
20-1.22-0.370.33-0.79-0.23
12RoeiselandMarte OlsbuNOR
14-1.47-0.82-1.04-1.23-0.23
13Hojnisz-StaregaMonikaPOL
18-1.09-1.100.02-0.96-0.22
14TandrevoldIngrid LandmarkNOR
21-1.15-0.68-0.23-0.90-0.21
15AymonierCeliaFRA
15-1.42-0.080.59-0.79-0.21
16MakarainenKaisaFIN
21-1.56-0.240.42-0.94-0.20
17GasparinElisaSUI
15-0.46-0.47-0.58-0.47-0.18
18HerrmannDeniseGER
21-1.66-0.21-0.30-1.07-0.18
19Kristejn PuskarcikovaEvaCZE
16-0.48-0.64-0.67-0.55-0.18
20ZbylutKingaPOL
15-0.35-0.37-0.35-0.36-0.17
21SimonJuliaFRA
21-1.05-0.24-1.69-0.89-0.16
22HaeckiLenaSUI
18-1.040.49-1.49-0.65-0.15
23OebergHannaSWE
19-1.14-0.94-1.57-1.14-0.14
24Yurlova-PerchtEkaterinaRUS
19-0.82-0.74-1.01-0.82-0.13
25BescondAnaisFRA
20-1.03-0.55-0.34-0.81-0.11
26PerssonLinnSWE
18-0.89-0.91-0.13-0.81-0.10
27WiererDorotheaITA
21-1.24-0.73-1.43-1.11-0.09
28BrorssonMonaSWE
18-0.80-0.74-0.03-0.69-0.06
29HorchlerKarolinGER
14-0.41-1.01-0.59-0.61-0.06
30HauserLisa TheresaAUT
18-0.63-0.99-1.06-0.78-0.04
31FialkovaPaulinaSVK
19-1.01-0.62-0.39-0.83-0.02
32HinzVanessaGER
19-0.85-0.69-0.45-0.75+0.01
33EganClareUSA
14-0.79-0.640.09-0.64+0.01
34OjaReginaEST
15-0.06-0.29-0.79-0.21+0.02
35KryukoIrynaBLR
18-0.69-0.690.06-0.60+0.07
36DunkleeSusanUSA
14-0.720.20-0.61-0.44+0.14
37VittozziLisaITA
21-0.92-0.40-0.82-0.76+0.24
Posted in Statistical analysis | Tagged 2019–20 season, overall performance, shooting, skiing

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

real biathlon is back

Posted on 2020-11-13 | by real biathlon | 2 Comments on real biathlon is back

After a long absence, it’s time to revive this website. I had always hoped to get back into posting biathlon statistics – if I found a way to automate the process more. Setting that up, however, seemed like a daunting task that would require a whole lot of time and work. So when I found myself stuck at home during the pandemic in the spring, I thought why not give this a try, especially since I had become much more experienced with programming and now had a background in computer science.

After countless hours and lots of work, the new and improved real biathlon website is finally ready for the upcoming season. Some of the more interesting features I came up with:

  • Athlete data for all available stats categories, including comparisons between athletes
  • Team results and statistics for all nations, including mixed or combined teams and team comparisons
  • Aggregate data for every season, every World Cup trimester, every championship and every single World Cup (e.g. men’s skiing stats for Antholz 2020 WCH or women’s shooting times of the 2019–20 season)
  • Skiing, shooting, range and loop times, as well as charts, for every race since 2001–02 (absolute or relative to specific athlete)
  • Charts showing the progress during a race/split times (absolute or relative to specific athlete)
  • Shooting intervals and patterns of all races since 2016–17
  • World Cup score tables and charts for every World Cup globe ever
  • All-time records for athletes and teams, at World Cup, World Championship or Olympic level (for every discipline), plus World Cup titles

The first hurdle was building an entirely new biathlon database, compiling all Biathlon World Cup data I could get my hands on. It turned into a much bigger project than I had anticipated, primarily because of the many inconsistencies in official data from the International Biathlon Union (IBU), which made it a real nightmare to get consistent results.

Eventually, I managed to merge all relevant IBU results with data from secondary sources (specifically the SQLite database of the Run&Shoot App, developed by Vladimir Filatov, as well as results on different editions of Wikipedia). While some data errors undoubtedly remain (that’s almost unavoidable with a data set of this size: 2199 top-level races featuring 3342 different athletes), I have collected quite reliable data for:

  • Every World Championship and Olympic race (since 1958)
  • Results for every World Cup race since the very first season in 1977–78 (albeit only partial results for some of the early seasons, but every podium finisher ever)
  • Shooting results since the 1980s
  • Skiing and shooting times since 2001–02
  • Split times/shooting intervals since 2016–17

Making the collected data available online was another big challenge. I’m not a web developer, so this website won’t win any design awards, but I did my best to make it as easy to use as possible; the main focus however was functionality. Special thanks to the members of our forum who helped with testing.

Keeping my fingers crossed that the 2020–21 Biathlon World Cup season will go ahead as planned, I’ll try my best to update all data as quickly as possible after each race. I hope that you will find this new website useful and full of interesting information, and you will enjoy exploring its features and statistics during the upcoming biathlon winter.

Posted in Website updates

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

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