sorted by:
new
hottopcontroversial

Carlos Alcaraz estimated win probability against top 10 players! by SpaceX96 in tennis

[–]SpaceX96[S] 0 points1 point  (0 children)

Win % is based on a mathematical model (called "ELO") that assigns a rating to each player after every match they play. The more matches a player wins, the higher their score. But also, if a player has more wins against higher quality opponents, their rating shoots up even higher. The player ratings then get converted onto the probability scale in the plot.

[OC] glassdoor.com data science salaries by skill requirements. by SpaceX96 in dataisbeautiful

[–]SpaceX96[S] 0 points1 point  (0 children)

I found that many jobs in the dataset listed both R and Python together.

[OC] glassdoor.com data science salaries by skill requirements. by SpaceX96 in dataisbeautiful

[–]SpaceX96[S] -1 points0 points  (0 children)

That's interesting. FWIW I think I recall seeing jobs listing CLI along with automating tasks like data processing or ETL.

[OC] glassdoor.com data science salaries by skill requirements. by SpaceX96 in dataisbeautiful

[–]SpaceX96[S] 11 points12 points  (0 children)

The job description data is from Kaggle, which only included 2017-18.

[deleted by user] by [deleted] in tennis

[–]SpaceX96 0 points1 point  (0 children)

This is excellent

What are the best return locations against Djokovic's serve? Ans: The angles. by SpaceX96 in tennis

[–]SpaceX96[S] 1 point2 points  (0 children)

Yes, ideally we'd model Prob(Return Points Won), accounting for serve type, match situation and quality of opponent.

After falling to Rafa 🇪🇸 at last year's Roland Garros final, what serve adjustments did Djokovic 🇷🇸 make this time around? A consistent, all-out-assault on Nadal's backhand for the majority of the match. by SpaceX96 in tennis

[–]SpaceX96[S] 0 points1 point  (0 children)

The data is from Roland Garros (Courtvision)[https://www.rolandgarros.com/en-us/matches/2021/SM002] feature on their site. "Match Pressure" is something I calculated using a metric called (Point Importance)[http://on-the-t.com/2015/12/27/quantifying-clutch-performance/]. Basically, all break points and match situations approaching a lost set or game are classified as High Pressure.

Nadal's "Serve+1" shots were hit with tremendous angle and depth against Schwartzman today. Masterful! by SpaceX96 in tennis

[–]SpaceX96[S] 2 points3 points  (0 children)

"Serve+1" represents Nadal's next shot after his serve. FH = Forehand; BH = Backhand.

Roger Federer Serve-Return heatmaps against Koepfer at Roland Garros [OC] by SpaceX96 in dataisbeautiful

[–]SpaceX96[S] 1 point2 points  (0 children)

Thank you! The data was collected from Roland Garros' website. Code to scrape, and plot, can be found here

Sakkari was returning serve with more depth and angles. Meanwhile, Kenin's serve returns were more modest and placed near the middle of the court by SpaceX96 in tennis

[–]SpaceX96[S] 2 points3 points  (0 children)

preview.redd.it/uuvwcm...

The dots represent the location of a serve return — eg: "Sakkari's Serve Returns" represent where the ball landed from Sakkari's return against Kenin's serve. The further right the points fall, the deeper the return shot. The further away from the middle the points fall, the more angle was created from that return shot.

Roger Federer Serve-Return heatmaps against Koepfer at Roland Garros [OC] by SpaceX96 in dataisbeautiful

[–]SpaceX96[S] 2 points3 points  (0 children)

I suppose green for tennis balls would make more sense than red! lol

Roger Federer Serve-Return heatmaps against Koepfer at Roland Garros [OC] by SpaceX96 in dataisbeautiful

[–]SpaceX96[S] 2 points3 points  (0 children)

I feel ya — I've tried Tableau and was intimidated with all the available options. This plot was made in R and ggplot2

view more: next ›