Tier-specific evolution of match performance characteristics in the English Premier League: it's getting tougher at the top.

This study investigated the evolution of physical and technical performances in the English Premier League (EPL), with special reference to league ranking. Match performance observations (n = 14,700) were collected using a multiple-camera computerised tracking system across seven consecutive EPL seasons (2006-07 to 2012-13). Final league rankings were classified into Tiers: (A) 1st-4th ranking (n = 2519), (B) 5th-8th ranking (n = 2965), (C) 9th-14th ranking (n = 4448) and (D) 15th-20th ranking (n = 4768). Teams in Tier B demonstrated moderate increases in high-intensity running distance while in ball possession from the 2006-07 to 2012-13 season (P < 0.001; effect size [ES]: 0.68), with Tiers A, C and D producing less pronounced increases across the same period (P < 0.005; ES: 0.26, 0.41 and 0.33, respectively). Large increases in sprint distance were observed from the 2006-07 to 2012-13 season for Tier B (P < 0.001; ES: 1.21), while only moderate increases were evident for Tiers A, C and D (P < 0.001; ES: 0.75, 0.97 and 0.84, respectively). Tier B demonstrated large increases in the number of passes performed and received in 2012-13 compared to 2006-07 (P < 0.001; ES: 1.32-1.53) with small-to-moderate increases in Tier A (P < 0.001; ES: 0.30-0.38), Tier C (P < 0.001; ES: 0.46-0.54) and Tier D (P < 0.001; ES: 0.69-0.87). The demarcation line between 4th (bottom of Tier A) and 5th ranking (top of Tier B) in the 2006-07 season was 8 points, but this decreased to just a single point in the 2012-13 season. The data demonstrate that physical and technical performances have evolved more in Tier B than any other Tier in the EPL and could indicate a narrowing of the performance gap between the top two Tiers.

Football Movement Profile-Based Creatine-Kinase Prediction Performs Similarly to Global Positioning System-Derived Machine Learning Models in National-Team Soccer Players.

PURPOSE: The relationship between external load and creatine-kinase (CK) response at the team/position or individual level using Global Positioning Systems (GPS) has been studied. This study aimed to compare GPS-derived and Football Movement Profile (FMP) -derived CK-prediction models for national-team soccer players. The second aim was to compare the performance of general and individualized CK prediction models. METHODS: Four hundred forty-four national-team soccer players (under 15 [U15] to senior) were monitored during training sessions and matches using GPS. CK was measured every morning from whole blood. The players had 19.3 (18.1) individual GPS-CK pairs, resulting in a total of 8570 data points. Machine learning models were built using (1) GPS-derived or (2) FMP-based parameters or (3) the combination of the 2 to predict the following days' CK value. The performance of general and individual-specific prediction models was compared. The performance of the models was described by R2 and the root-mean-square error (RMSE, in units per liter for CK values). RESULTS: The FMP model (R2 = .60, RMSE = 144.6 U/L) performed similarly to the GPS-based model (R2 = .62, RMSE = 141.2 U/L) and the combination of the 2 (R2 = .62, RMSE = 140.3 U/L). The prediction power of the general model was better on average (R2 = .57 vs R2 = .37) and for 73% of the players than the individualized model. CONCLUSIONS: The results suggest that FMP-based CK-prediction models perform similarly to those based on GPS-derived metrics. General machine learning models' prediction power was higher than those of the individual-specific models. These findings can be used to monitor postmatch recovery strategies and to optimize weekly training periodization.

Football movement profile analysis and creatine kinase relationships in youth national team players.

Objective: Creatine kinase (CK) is widely used as a monitoring tool to make inferences on fatigue and readiness in elite soccer. Previous studies have examined the relationship between CK and GPS parameters, however these metrics may not accurately describe the players' load during soccer-specific movements. Football Movement Profile (FMP) monitoring is a viable option for such purposes, providing solely inertial sensor-based data and categorizing movements according to intensity (very low, low, medium, high) and movement type (running-linear locomotive, dynamic - change of direction or speed). Methods: We investigated the relationship between the FMP distribution of youth (U16-U21) national team soccer players and the absolute day-to-day change in CK. We applied Spearman's correlations, principal component analysis and K-means clustering to classify players' CK responses according to their specific FMP. Results: Moderate to large negative associations were found between very low intensity FMP parameters and CK change (r = -0.43 ± 0.12) while large positive associations were identified between CK change and other FMP metrics (r = 0.62 ± 0.12). Best fitting clustering methods were used to group players depending on their CK sensitivity to FMP values. Principal component analysis explained 83.0% of the variation with a Silhouette score of 0.61 for the 4 clusters. Conclusions: Our results suggest that soccer players can be clustered based on the relationship between FMP measures and the CK change. These findings can help to plan soccer training or recovery sessions according to the desired load on skeletal muscle, as FMP monitoring might bridge the limitations of GPS telemetry.

Individual-Specific Relationship Between External Training and Match Load and Creatine-Kinase Response in Youth National Team Soccer Players.

BACKGROUND: Previous studies have examined the relationship between external load and creatine-kinase (CK) response at the team level. This study aimed to build individualized CK prediction models for elite youth national team soccer players. HYPOTHESIS: The CK response of youth soccer players can be categorized as being sensitive to micromovements (MM), high-velocity (HV) parameters, or the combination of both, measured during training sessions and matches. STUDY DESIGN: Prospective cohort study. LEVEL OF EVIDENCE: Level 4. METHODS: A total of 25 U16-U17 youth national team soccer players were monitored during training sessions and matches using global positioning system (GPS) units. Individual CK values were measured every morning from whole blood. The data set consisted of 57 ± 17 individual datapoints per player. Individual prediction models were used to examine the relationship between external load and consecutive CK changes. Numerous models were built for each player using MM, HV parameters, or the combination of both. The performance of the models was described by the R2 and the root mean square error (RMSE, U/l for CK values). RESULTS: The MM models were superior for 8 players (R2 = 0.68; RMSE = 113 U/l), followed by HV (8 players; R2 = 0.69; RMSE = 88 U/l) and the combined models (2 players; R2 = 0.64; RMSE = 141 U/l). For the remaining 7 players, the R2 of the models was <0.5. The recovery time between efforts was more important in the HV model. CONCLUSION: Players could be categorized on sensitivity to MM, HV movements, or the combination of both. CLINICAL RELEVANCE: These findings can be used to individualize postmatch recovery strategies and to optimize weekly training periodization to maximize match performance.

Quantification of Training Load Relative to Match Load of Youth National Team Soccer Players.

BACKGROUND: Previous studies have examined the training load relative to match load in club settings. The aims of this study were to (1) quantify the external training load relative to match load in days before a subsequent international game and (2) examine the cumulative training load in relation to match load of U-17 national team field soccer players. HYPOTHESIS: Volume and intensity load parameters will vary between trainings; the farthermost trainings have the highest load gradually decreasing toward the match. STUDY DESIGN: Prospective cohort study. LEVEL OF EVIDENCE: Level 4. METHODS: External training load data were collected from 84 youth national team players using global positioning technology between 2016 and 2020. In the national team setting, training load data were obtained from 3 days before the actual match day (MD-3, MD-2, MD-1 days) and analyzed with regard to the number of days up to the game. Volume and intensity parameters were calculated as a percentage of the subsequent match load. RESULTS: Significant differences were found between MD-1 and MD-2, as well as between MD-1 and MD-3 for most volume parameters (P < 0.01; effect sizes [ESs] 0.68-0.99) and high-intensity distance (P < 0.002; ES 0.67 and 0.73) and maximum velocity (P < 0.002; ES 0.82) as intensity parameters. Most cumulative values were significantly different from total duration (P < 0.001, common language ES 0.80-0.96). CONCLUSION: The training volume gradually decreased as match day approached, with the highest volume occurring on MD-3. Intensity variables, such as maximum velocity, high-intensity accelerations, and meterage per minute were larger in MD-1 training relative to match load. Training volume was lowest in MD-1 trainings and highest in MD-3 trainings; intensity however varies between training days. CLINICAL RELEVANCE: The findings of this study may help to understand the special preparational demands of international matches, highlighting the role of decreased training volume and increased intensity.

Factors Influencing Creatine Kinase Response in Youth National Team Soccer Players.

BACKGROUND: Previous studies have examined the relationship between external training load and creatine kinase (CK) response after soccer matches in adults. This study aimed to build training- and match-specific CK prediction models for elite youth national team soccer players. HYPOTHESIS: Training and match load will have different effects on the CK response of elite youth soccer players, and there will be position-specific differences in the most influential external and internal load parameters on the CK response. STUDY DESIGN: Prospective cohort study. LEVEL OF EVIDENCE: Level 4. METHODS: Forty-one U16-U17 youth national team soccer players were measured over an 18-month period. Training and match load were monitored with global positioning system devices. Individual CK values were measured from whole blood every morning in training camps. The dataset consisted of 1563 data points. Clustered prediction models were used to examine the relationship between external/internal load and consecutive CK changes. Clusters were built based on the playing position and activity type. The performance of the linear regression models was described by the R2 and the root-mean-square error (RMSE, U/L for CK values). RESULTS: The prediction models fitted similarly during games and training sessions (R2 = 0.38-0.88 vs 0.6-0.77), but there were large differences based on playing positions. In contrast, the accuracy of the models was better during training sessions (RMSE = 81-135 vs 79-209 U/L). Position-specific differences were also found in the external and internal load parameters, which best explained the CK changes. CONCLUSION: The relationship between external/internal load parameters and CK changes are position specific and might depend on the type of session (training or match). Morning CK values also contributed to the next day's CK values. CLINICAL RELEVANCE: The relationship between position-specific external/internal load and CK changes can be used to individualize postmatch recovery strategies and weekly training periodization with a view to optimize match performance.