ArticleBiology of sport2024
Tell me how much your opponent team runs and I will tell you how much you should run: A predictive model applied to Spanish high-level football.
Article in Biology of sport, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 4 citations in OpenAlex.
- Can Locomotor Performance Predict the Final Result of a Football Match? A Machine Learning Approach.Sensors (Basel, Switzerland) · 2026Article
- Locomotor characteristics of intense accelerations according to the playing position in top Spanish football teams during competition.Biology of sport · 2025Article
- Multivariate analysis of teams' physical performances in official football matches in LaLiga 2023-24.Biology of sport · 2025Article
- Forecasting extremes of football players' performance in matches.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this study was to predict a team's accumulated distance (TotDisTea) and accumulated distance at > 21 km/h (TotDis21Tea) in the Spanish Football First Division. 2,946 team physical performances (out of 3040 possible) during four seasons (from 2016-17 to 2019-20) were analysed. The outcome variables were the TotDisTea and TotDis21Tea when the ball was in play. Eight predictor variables were used: the distance accumulated and accumulated at > 21 km/h by the opponent (TotDisOpp and TotDis21Opp) were registered in km, the effective playing (EffPlaTim) and possession (BalPos) time were recorded in min, match location (MatLoc) had two levels (home and away), match outcome (MatOut) had three levels (lost, drawn, and won), and the teams were grouped in four levels (Champions League, Europa League, remained, and relegation) distinguishing the observed team (TeaLev) and the opponent team (OppLev) in the match. A total of 127 models were estimated from the all-possible regressions procedure for each outcome variable. The model with six predictor variables was selected as the best model to predict the TotDisTea (
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.