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In high-performance sport, science and medicine practitioners employ a variety of physical and psychological tests, training and match monitoring, and injury-screening tools for a variety of reasons, mainly to predict performance, identify talented individuals, and flag when an injury will occur. The ability to “predict” outcomes such as performance, talent, or injury is arguably sport science and medicine’s modern-day equivalent of the “Quest for the Holy Grail.” The purpose of this invited commentary is to highlight the common misinterpretation of studies investigating association to those actually analyzing prediction and to provide practitioners with simple recommendations to quickly distinguish between methods pertaining to association and those of prediction.

There is a common expression in sports that “there is no ‘I’ in team.” However, collectively, there is actually a very important “I” in sport teams—the individual athlete/player. Each player has his or her own unique characteristics including physical, physiological, and psychological traits. Due to these unique characteristics, each player requires individual provision—whether it be an injury risk profile and targeted prevention strategy or treatment/rehabilitation for injury, dietary regimen, recovery, or psychological intervention. The aim of this commentary is to highlight how 4 high-performance teams from various professional football codes are analyzing individual player data.

Purpose: To analyze the relationships between the recovery kinetics experienced by professional football players and noncontact injury. Methods: A cohort of 46 professional football players (age 24.2 [4.7] y) from the same team (French Ligue 1) was monitored each day between matches when the team played twice a week. The recovery monitoring procedure was implemented after 38 matches and included some questionnaires: duration of sleep, Hooper scale (quality of sleep, level of stress, fatigue, and muscle soreness), perceived recovery status scale, creatine kinase concentrations, a countermovement jump, and an isometric force test. Noncontact injuries were collected during these periods. Results: Noncontact injuries were associated with perceived fatigue and muscle soreness 2 days (relative risk [RR] = 1.89 and 1.48, respectively) and 3 days following the matches (RR = 2.08 and 2.08, respectively). An increase of sleep quantity during the 2 nights following a match was significantly associated with a lower RR (RR = 0.65), as well as a lower decrement score on the isometric force test on each of the 3 days after the matches (RR = 0.97, RR = 0.99, and RR = 0.97, respectively). No other association was reported for the variables sleep quality, stress, perceived recovery, creatine kinase concentrations, countermovement jump, and noncontact injuries. Conclusion: During a congested schedule, implementing a recovery monitoring protocol including questionnaires about fatigue, muscle soreness, quantity of sleep, and a physical test of isometric force could help practitioners prevent injuries.

Purpose: This study examined posttravel perceptual responses of national-team footballers (soccer) following different flight paths, arrival/departure times, and trip contexts. Methods: Details of 396 flights from national-team players (N = 68) were obtained and verified via an online flight database. Each player provided ratings of perceptual fatigue, sleep, soreness, stress, and jet lag for 2 days before and after each trip. The flight path (continents of departure and arrival), travel context (into vs out of national team), and arrival and departure times were obtained for each trip. Linear mixed models compared the pretravel with posttravel change in perceptual responses based on flight path, context, and schedule. Results: Perceived jet-lag ratings were more responsive to travel variables (R ² = .48) than other perceptual ratings (R ² < .26). Travel from Asia to Europe (P < .05) and Europe to Australia (P < .001) had significantly higher jet-lag ratings than all other paths. Fatigue scores were worst following Asia to Europe (P < .05) and Europe to Australia (P < .05) travel, while sleep scores were worst following Europe to Australia travel (P < .01). Perceptual responses were poorer following travel from national team to club compared with all other travel contexts (P < .05). Arrival during the daytime (11 AM to 5 PM) resulted in better perceptual responses than early-morning or late-night arrivals (P < .05). Conclusions: Perceived jet-lag ratings are more responsive to travel demands than perceptual wellness scales in national-team football athletes. Poorer perceptual responses may be expected when travel is longer in nature, ends later in the day, or involves travel out of the national team back to club.

Purpose: This study investigated the association between (1) time zone difference and (2) travel direction (east vs west) with posttravel changes in perceptual responses of national-team footballers. Methods: Travel schedules from 355 national-team trips (50 elite soccer players) were verified using an online flight database. All players provided perceptual ratings of fatigue, sleep quality, soreness, and stress to calculate changes in scores up to 2 days after travel. Trips were categorized as <3, 3 to 6, 6 to 9, or 9+ time zone changes, along with travel direction (eastward or westward). The pretravel to posttravel changes in perceptual ratings at days 1 and 2 postarrival were compared between time zone change and travel direction with linear mixed models. Results: For every time zone crossed, poorer ratings of perceptual fatigue (β = 0.068, P < .001), sleep (β = 0.095, P < .001), soreness (β = 0.0049, P < .001), and total wellness (β = 0.214, P < .001) were observed. However, the models explained only small proportions of the variation in postflight perceptual responses (7%–18%). Regardless, travel across 9+ time zones resulted in significantly worse perceived fatigue, sleep, and total wellness for days 1 and 2 postarrival compared with travel with <6 time zones (P < .05). Additionally, fatigue, sleep, and total scores were worse on day 2 following trips of 9+ time zones. Eastward travel resulted in poorer sleep ratings (β = 0.52, P < .001) than westward travel within time zone groupings. Conclusions: Perceptual ratings of fatigue and sleep become progressively worse as travel increases in national-team soccer players, especially after travel across 9+ time zones and eastward travel.

Background: Athlete-reported outcome measures (AROMs) are frequently used in research and practice but no studies have examined their psychometric properties. Objectives: Part 1—identify the most commonly used AROMs in sport for monitoring training responses; part 2—assess risk of bias, measurement properties, and level of evidence, based on the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) guidelines. Study Appraisal and Synthesis Methods: Methodological quality of the studies, quality of measurement properties, and level of evidence were determined using the COSMIN checklist and criteria. Results: Part 1—from 9446 articles screened for title and abstract, 310 out of 334 full texts were included; 53.9% of the AROMs contained multiple items, while 46.1% contained single items. Part 2—from 1895 articles screened for title and abstract, 71 were selected. Most measurement properties of multiple-item AROMs were adequate, but content validity and measurement error were inadequate. With the exclusion of 2 studies examining reliability and responsiveness, no validity studies were found for single items. Conclusions: The measurement properties of multiple-item AROMs derived from psychometrics were acceptable (with the exclusion of content validity and measurement error). The single-item AROMs most frequently used in sport science have not been validated. Additionally, nonvalidated modified versions of the originally nonvalidated items are common. Until proper validation studies are completed, all conclusions based on these AROMs are questionable. Established reference methods, such as those of clinimetrics, should be used to develop and assess the validity of AROMs.

Purpose: To survey soccer practitioners’ recovery strategy: (1) use, (2) perceived effectiveness, and (3) factors influencing their implementation in professional soccer. Methods: A cross-sectional convenience sample of professional soccer club/confederation practitioners completed a web-based survey (April to July 2020). Pearson chi-square and Fisher exact tests with Cramer V (φ − c) assessed relationships and their strength, respectively, between the perceived effectiveness and frequency of strategy use. Results: A total of 80 soccer practitioners (13 countries) completed the survey. The 3 most important recovery objectives were “alleviating muscle damage/fatigue,” “minimizing injury risk,” and “performance optimization.” The most frequently used strategies were active recovery, structured recovery day, extra rest day, massage, cold-water therapy, and carbohydrate provision (predominantly on match day and match day + 1). Relationships were identified between perceived effectiveness and frequency of strategy use for sleep medication (P < .001, φ − c = 0.48), carbohydrate provision (P = .007, φ − c = 0.60), protein provision (P = .007, φ − c = 0.63), an extra rest day (P < .001, φ − c = 0.56), and a structured recovery day (P = .049, φ − c = 0.50). Conclusions: The study demonstrates that professional soccer practitioners have a range of objectives geared toward enhancing player recovery. A disconnect is apparent between the perceived effectiveness of many recovery strategies and their frequency of use in an applied setting. Novel data indicate that strategies are most frequently employed around match day. Challenges to strategy adoption are mainly competing disciplinary interests and resource limitations. Researchers and practitioners should liaise to ensure that the complexities involved with operating in an applied environment are elucidated and apposite study designs are adopted, in turn, facilitating the use of practically effective and compatible recovery modalities.

Purpose: To examine the construct validity and reliability of 2 single items for fatigue and recovery in dancers. The construct validity was assessed using reference instruments: the fatigue items of the Brunel Mood Scale (BRUMS) and the Short Recovery and Stress Scale (SRSS). A secondary aim was to explore the respondent interpretation of these 2 items using a concept identification approach. Methods: Two hundred forty-three (N = 243) dancers completed single-item fatigue and recovery (unipolar and bipolar), BRUMS, and SRSS once for construct validity. For reliability,  49 dancers completed the questionnaires twice, 1 week apart. Using a concept identification approach, 49 dancers were also asked comprehension and interpretation of fatigue and recovery. Results: The fatigue item correlated with SRSS stress items (r _s = .37–.51) and BRUMs fatigue items (r _s = .63–.66). The recovery item was only partially confirmed in terms of construct validity, when using the SRSS recovery items as reference (r _s = .39–.43). Reliability was confirmed for the single items of fatigue (κ = .77–.78) and recovery (κ = .71–.78). Main responses for the concept of fatigue were tiredness (34.7%), muscle soreness (17.3%), and energy (13.0%). Main responses for the concept of recovery were muscle soreness (43.0%), tiredness (27.9%), and fatigue (24.0%). Conclusion: We provide preliminary confirmation of the validity and reliability of the single item fatigue in dancers. The recovery item was only partially confirmed in terms of construct validity, when using the SRSS recovery items as reference, but did display acceptable reliability and agreement. Further research is warranted further exploring other measurement properties.