45,942 responses from 2,386 respondents to 4 items.
| Description | Soccer shots from Wyscout match event logs, one row per shot: 45,942 shots by 2,386 players in every match of the 2017-18 English, French, German, Italian and Spanish first divisions, the 2018 World Cup and Euro 2016 (1,941 matches). id = player (Wyscout id); item = kind of shot (foot, head, free_kick, penalty); resp = 1 if a goal. Players choose when and where to shoot, so conversion rates mix finishing skill with shot selection; shot location in trial_locx/trial_locy (percent of pitch) with distance (m) and goal-mouth angle derived. trial_ columns also carry competition, match id, game clock, shoot-out flag, home/away (league matches only) and counter-attack flag. Real-data counterpart to the simulated nbashots_sim. |
|---|---|
| Reference | Pappalardo, L., Cintia, P., Rossi, A., Massucco, E., Ferragina, P., Pedreschi, D., & Giannotti, F. (2019). A public data set of spatio-temporal match events in soccer competitions. Scientific Data, 6, 236. |
| DOI | 10.1038/s41597-019-0247-7 |
| Licence | CC BY 4.0 |
| Source data | https://doi.org/10.6084/m9.figshare.c.4415000 |
| Responses | 45,942 |
|---|---|
| Respondents | 2,386 |
| Items | 4 |
| Response categories | 2 |
| Responses per respondent | 19.255 |
| Responses per item | 11485.500 |
| Density | 4.814 |
| Longitudinal | TRUE |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, wyscout_shots.R. It is our best reading, not the source’s own codebook, and it may contain mistakes. Where the two disagree, the source data are the authority. If you find an error, please tell us.
| Column | What it is | Source column |
|---|---|---|
| IRW standard columns | ||
date | IRW standard Calendar time in seconds — either seconds elapsed since data collection start (relative) or Unix time (absolute). | made in the build script |
id | IRW standard Identifier for the focal unit being measured — typically a person, but sometimes another entity (e.g., a word in a lexical task). | t |
item | IRW standard Item identifier. | made in the build script |
resp | IRW standard Response value. | made in the build script |
| Other columns | ||
trial_angle | Not documented Trial-level details in repeated-trial designs: the trial index (trial_number), block, list, and so on. | made in the build script |
trial_competition | Not documented Trial-level details in repeated-trial designs: the trial index (trial_number), block, list, and so on. | made in the build script |
trial_counter | Not documented Trial-level details in repeated-trial designs: the trial index (trial_number), block, list, and so on. | made in the build script |
trial_dist | Not documented Trial-level details in repeated-trial designs: the trial index (trial_number), block, list, and so on. | made in the build script |
trial_gameclock | Not documented Trial-level details in repeated-trial designs: the trial index (trial_number), block, list, and so on. | made in the build script |
trial_home | Not documented Trial-level details in repeated-trial designs: the trial index (trial_number), block, list, and so on. | made in the build script |
trial_locx | Not documented Trial-level details in repeated-trial designs: the trial index (trial_number), block, list, and so on. | made in the build script |
trial_locy | Not documented Trial-level details in repeated-trial designs: the trial index (trial_number), block, list, and so on. | made in the build script |
trial_match | Not documented Trial-level details in repeated-trial designs: the trial index (trial_number), block, list, and so on. | made in the build script |
trial_number | Not documented Trial-level details in repeated-trial designs: the trial index (trial_number), block, list, and so on. | made in the build script |
trial_shootout | Not documented Trial-level details in repeated-trial designs: the trial index (trial_number), block, list, and so on. | made in the build script |
What item codes and covariate values stand for is in the codebook released with the source data.
Or load it directly in R or Python (both need a free Redivis account; the first call opens a browser to sign in):
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("wyscout_shots")
# Python
pip install irw
import irw
df = irw.fetch("wyscout_shots")
@article{Pappalardo_2019, title={A public data set of spatio-temporal match events in soccer competitions}, volume={6}, ISSN={2052-4463}, url={http://dx.doi.org/10.1038/s41597-019-0247-7}, DOI={10.1038/s41597-019-0247-7}, number={1}, journal={Scientific Data}, publisher={Springer Science and Business Media LLC}, author={Pappalardo, Luca and Cintia, Paolo and Rossi, Alessio and Massucco, Emanuele and Ferragina, Paolo and Pedreschi, Dino and Giannotti, Fosca}, year={2019}, month=Oct }
| IRW version | v622 |
|---|---|
| Redivis dataset | item_response_warehouse_6 v5.11 |
| Redivis dataset DOI | 10.57761/qahq-j855 |
| Manifest pin for this IRW version | v5.11 |
| Metadata source | irw_meta v49.0 |