Examining the External Training Load of an English Premier League Football Team With Special Reference to Acceleration

2016 ◽  
Vol 30 (9) ◽  
pp. 2424-2432 ◽  
Author(s):  
Richard Akenhead ◽  
Jamie A. Harley ◽  
Simon P. Tweddle
Keyword(s):  
Special Reference ◽  
Training Load ◽  
Premier League ◽  
Football Team ◽  
External Training ◽  
English Premier League

scholarly journals Seasonal Training-Load Quantification in Elite English Premier League Soccer Players

2015 ◽  
Vol 10 (4) ◽  
pp. 489-497 ◽  
Author(s):  
James J. Malone ◽  
Rocco Di Michele ◽  
Ryland Morgans ◽  
Darren Burgess ◽  
James P. Morton ◽  
...  
Keyword(s):  
Heart Rate ◽  
Training Load ◽  
Soccer Players ◽  
Maximal Heart Rate ◽  
Time Points ◽  
Total Distance ◽  
Professional Soccer ◽  
Premier League ◽  
Daily Training ◽  
English Premier League

Purpose:To quantify the seasonal training load completed by professional soccer players of the English Premier League.Methods:Thirty players were sampled (using GPS, heart rate, and rating of perceived exertion [RPE]) during the daily training sessions of the 2011–12 preseason and in-season period. Preseason data were analyzed across 6 × 1-wk microcycles. In-season data were analyzed across 6 × 6-wk mesocycle blocks and 3 × 1-wk microcycles at start, midpoint, and end-time points. Data were also analyzed with respect to number of days before a match.Results:Typical daily training load (ie, total distance, high-speed distance, percent maximal heart rate [%HRmax], RPE load) did not differ during each week of the preseason phase. However, daily total distance covered was 1304 (95% CI 434–2174) m greater in the 1st mesocycle than in the 6th. %HRmax values were also greater (3.3%, 1.3−5.4%) in the 3rd mesocycle than in the first. Furthermore, training load was lower on the day before match (MD-1) than 2 (MD-2) to 5 (MD-5) d before a match, although no difference was apparent between these latter time points.Conclusions:The authors provide the 1st report of seasonal training load in elite soccer players and observed that periodization of training load was typically confined to MD-1 (regardless of mesocycle), whereas no differences were apparent during MD-2 to MD-5. Future studies should evaluate whether this loading and periodization are facilitative of optimal training adaptations and match-day performance.

The Football Team Composition Problem: a Stochastic Programming approach

2017 ◽  
Vol 13 (3) ◽  
pp. 113-129 ◽  
Author(s):  
Giovanni Pantuso
Keyword(s):  
Programming Approach ◽  
Football Players ◽  
Steady Growth ◽  
Premier League ◽  
Football Team ◽  
Composition Problem ◽  
Football Clubs ◽  
European Football ◽  
English Premier League

Abstract Most professional European football clubs are well-structured businesses. Therefore, the financial performance of investments in players becomes crucial. In this paper, after the problem is discussed and formalized, an optimization model with the objective of maximizing the expected value of the team is presented. The model ensures that the team has the required mix of skills, that competition regulations are met, and that budget limits are respected. The model explicitly takes into account the uncertainty in the career development of football players. A case study based on the English Premier League is presented. Our results show that the model has significant potential to improve current decisions ensuring a steady growth of the value of the team. The team value growth reported is particularly driven by investments in young prospects.

Optimization of The Best Line-up in Football using Binary Integer Programming Model

2020 ◽  
Vol 1 (3) ◽  
pp. 114-122
Author(s):  
Mahrudinda . ◽  
Sudrajat Supian ◽  
Subiyanto . ◽  
Diah Chaerani
Keyword(s):  
Integer Programming ◽  
Programming Model ◽  
Binary Integer Programming ◽  
Optimum Result ◽  
Premier League ◽  
Maximum Value ◽  
Football Team ◽  
Selection Of Variables ◽  
English Premier League ◽  
Selection Of

This paper aims to find the formation with the best line-up of the Liverpool FC football team in the English Premier League in the 2020/2021 season. Researchers used binary integer programming (BIP) modeling to determine optimum solutions. The data used for this optimization is the rating value of the players recorded in the performance data from the previous matches. The optimum result of this problem is the selection of variables that are valued at 1, namely {x_1, x_4, x_6, x_8, x_21, x_28, x_34, x_37, and x_39} for formations 4-3-3 with a maximum value of 82.47, and variables {x_1, x_6, x_7, x_8, x_11, x_14, x_16, x_29, x_31, x_32, andx_42} for 4-2-3-1 formations with a maximum value of 80.04. The 4-3-3 formation is more effective because it has a higher maximum rating than the 4-2-3-1 formation.  4-3-3 formation is an attacking formation with a higher intensity of attack and faster than  4-2-3-1 formation that tends to defend moderately.

scholarly journals Optimization of The Best Line-up in Football using Binary Integer Programming Model

2020 ◽  
Vol 1 (3) ◽  
pp. 114-122
Author(s):  
Mahrudinda Mahrudinda ◽  
Sudrajat Supian ◽  
Subiyanto Subiyanto ◽  
Diah Chaerani
Keyword(s):  
Integer Programming ◽  
Programming Model ◽  
Binary Integer Programming ◽  
Optimum Result ◽  
Premier League ◽  
Maximum Value ◽  
Football Team ◽  
Selection Of Variables ◽  
English Premier League ◽  
Selection Of

This paper aims to find the formation with the best line-up of the Liverpool FC football team in the English Premier League in the 2020/2021 season. Researchers used binary integer programming (BIP) modeling to determine optimum solutions. The data used for this optimization is the rating value of the players recorded in the performance data from the previous matches. The optimum result of this problem is the selection of variables that are valued at 1, namely {𝑥1,𝑥4,𝑥6,𝑥8,𝑥11,𝑥18,𝑥21,𝑥28,𝑥34,𝑥37,dan 𝑥39 } for formations 4-3-3 with a maximum value of 82.47, and variables {𝑥1,𝑥6,𝑥7,𝑥8,𝑥11,𝑥14,𝑥16,𝑥29,𝑥31,𝑥32, dan 𝑥42 } for 4-2-3-1 formations with a maximum value of 80.04. The 4-3-3 formation is more effective because it has a higher maximum rating than the 4-2-3-1 formation. 4-3-3 formation is an attacking formation with a higher intensity of attack and faster than 4-2-3-1 formation that tends to defend moderately.

scholarly journals Internal and External Training Load in Under-19 versus Professional Soccer Players during the In-Season Period

2021 ◽  
Vol 18 (2) ◽  
pp. 558
Author(s):  
Sullivan Coppalle ◽  
Guillaume Ravé ◽  
Jason Moran ◽  
Iyed Salhi ◽  
Abderraouf Ben Abderrahman ◽  
...  
Keyword(s):  
Perceived Exertion ◽  
Training Session ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Soccer Players ◽  
Time Intervals ◽  
Professional Soccer ◽  
Global Positioning ◽  
External Training ◽  
Soccer Team

This study aimed to compare the training load of a professional under-19 soccer team (U-19) to that of an elite adult team (EAT), from the same club, during the in-season period. Thirty-nine healthy soccer players were involved (EAT [n = 20]; U-19 [n = 19]) in the study which spanned four weeks. Training load (TL) was monitored as external TL, using a global positioning system (GPS), and internal TL, using a rating of perceived exertion (RPE). TL data were recorded after each training session. During soccer matches, players’ RPEs were recorded. The internal TL was quantified daily by means of the session rating of perceived exertion (session-RPE) using Borg’s 0–10 scale. For GPS data, the selected running speed intensities (over 0.5 s time intervals) were 12–15.9 km/h; 16–19.9 km/h; 20–24.9 km/h; >25 km/h (sprint). Distances covered between 16 and 19.9 km/h, > 20 km/h and >25 km/h were significantly higher in U-19 compared to EAT over the course of the study (p =0.023, d = 0.243, small; p = 0.016, d = 0.298, small; and p = 0.001, d = 0.564, small, respectively). EAT players performed significantly fewer sprints per week compared to U-19 players (p = 0.002, d = 0.526, small). RPE was significantly higher in U-19 compared to EAT (p =0.001, d = 0.188, trivial). The external and internal measures of TL were significantly higher in the U-19 group compared to the EAT soccer players. In conclusion, the results obtained show that the training load is greater in U19 compared to EAT.

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