jeffsackmann / tennis_slam_pointbypoint Goto Github PK

Hi Jeff,

Appears that there are 67 items in each column, but only 65 column headers.

headers = "match_id,ElapsedTime,SetNo,P1GamesWon,...,ServeWidth,ServeDepth,ReturnDepth"
first_row = "2017-ausopen-1101,0:00:00,1,0,...,,,"
print(len(headers.split(",")))
>>> 65
print(len(first_row.split(",")))
>>> 67

The Grand Slams point-by-point data includes the following variables and entries:
ServeWidth = {"B", "BC", "BW", "C", "W"}
ServeDepth = {"CTL", "NCTL"}
ReturnDepth = {"D", "ND"}
ServeIndicator = {1, 2}
Serve_Direction = {0, 1, 2, 3}

Could I know what these mean? Thanks.

I'm getting a lot of conflation between men's and women's when I do entire point-level aggregations. I would like to separate them obviously but before I do so I want to run this by @JeffSackmann and any other maintainers to make sure nobody has a local fix for this already.

> df %>% group_by(event_name) %>% count
# A tibble: 5 × 2
# Groups:   event_name [5]
  event_name            n
  <chr>             <int>
1 event_MS          52105
2 event_WS          30912
3 Men's Singles     36666
4 Women's Singles   26380
5 NA              1526475

Wrt how to fix it I was thinking of a simply

1. Checking which matches went 4 or 5 sets (SetNo >= 4 for all points in a match_id) -> these are obviously men, fill event_name.
2. Checking which matches only went 2 sets (SetNo <= 2 for all points in a match_id) -> these are obviously women, fill event_name.
3. For the remaining ones (women's sets who went the distance or men's sets who only went three), check by the names which I have hopefully largely filled in from steps 1 and 2 just above.

Might you care to add a section in the README.md describing how the data was obtained?

Is there a way to get up-to-date data during a tournament (e.g. Wimbledon)?

And if so, what's the latency? end-of-day?

Hi,
does anyone have an idea what does History column stand for?

Hi, I just have a quick question. How are the P1 Momentum and P2 Momentum figures calculated?
Thanks!

Hi

Is there any possibility that we can avail timestamp data.

Hi @JeffSackmann, just wanted to say thanks for putting this together and sharing it with the world. I used your data from the 2015 Australian Open for a school project about Serena Williams and credited you.