1. ‘Contribution of planned and unplanned training to overall load in elite youth female football players'
Presenting Author: Jonathan Taylor
Co-Authors: Chris Hurst, Russ Best and Matthew Wright

2. Context Teesside Sport Girls Centre of Excellence
Sport Science Support:
Fitness testing
Pitch-based conditioning
Neuromuscular training
Training monitoring
Age groups: Under 11 – Under 17
Key issues:
Overreaching/Overtraining
Purpose of girls centre of excellence – top picture is aim i.e. get players playing for england.
How do we monitor all the different types of training/ fitness day to day.
We are the only centre that has a systematic approach like this.

3. Background
Planned manipulation of training load = essential to achieve desired adaptations (Borresson and Lambert, 2009).
Accurate assessment of training load facilitates training prescription (Wrigley et al. 2012)
Lack of research investigating the training load undertaken by young female football players.
Furthermore, research acknowledging any external training activities that elite youth athletes engage in is limited.
Failure of coaches to acknowledge these activities can be problematic!
Early specialization and ever increasing training loads increases injury risk in young athletes (Capranica and Millard-Stafford et al. 2011; Malisoux et al. 2013)
More information on the studies to emphasize the need for our study – Only in Male players and only planned training.

4. Aims
Investigate the contribution of ‘planned’ and ‘unplanned’ training to overall load.
Investigate within-season variation in training load.
Assess compliance rates of the players in providing training data derived from self-report measures.
Training Load

5. Methods Observational
35 female players (aged years) required to keep a training diary over a 34-week period.
Training load calculated as: Session duration * Global RPE = session-RPE (AU) (Foster et al ).
Training coded as ‘football’ and ‘strength and conditioning’ and ‘additional’ (i.e. school-based physical education sessions, or participation in other sports).
The contribution of these modes of training to overall training load was calculated.
Athlete compliance also assessed.

7. Statistical analysis
Only players providing >17 weeks of data were included in the analysis.
Data were analysed using a mixed linear model – most appropriate statistical technique when dealing large data sets with fixed (time-point) and random effects (individual players and training sessions) and in instances where data is missing (Malone et al. 2015)
The magnitude of effects was calculated using a customised spreadsheet (Hopkins, 2007)
Standardised thresholds for small, moderate and large changes of 0.2, 0.6 and 1.2 standard deviations, respectively were used (Hopkins et al. 2009).

8. Results Compliance rates
Compliance rates of players providing >17 weeks’ of data was 32%.
Weekly training load
Mean weekly training load was 2157 ± 454 arbitrary units (au).
Training mode
‘Likely small’ differences between ‘football’ and ‘strength and conditioning’ (290 au; 90% CL ±75); and ‘additional’ and ‘strength and conditioning’ (227 au; CL ±76).
‘Trivial’ differences were observed between ‘football’ and ‘additional’ training (63; ±76).
Larger within, than between player differences observed (559; ES 0.92; ±22; and 203; ES 0.34; ±66 respectively).

9. Results
Figure 1. Mean contribution of training mode to overall load across the season

10. Strength and Conditioning
Results
Table 1. Within-season variation in training load (mean load for all players).
Phase of Season
Total
Football
Strength and Conditioning
Additional
Early- (Aug-Nov)
2401 ± 413
974 ± 316
623 ± 136
804 ± 302
Mid- (Nov-Feb)
1888 ± 530
643 ± 351
558 ± 153
687 ± 238
Late- (Feb-April)
2169 ± 185
866 ± 304
443 ± 102
870 ± 211
All data expressed as arbitrary units with mean ± standard deviation presented

11. Strength and Conditioning
Table 2. Effect of the phase of season (Early-, Mid- and Late-) on training load.
Difference (au)
90% Confidence Interval
Effect
Total
Early-Mid
-462
-282 to -643
Very Likely Small ↘
Mid-Late
411
643 to 187
Very Likely Small ↗
Early-Late
-52
165 to 268
Possibly Trivial ↘
Football
-319
-213 to -426
Possibly Moderate↘
249
382 to 117
69.9
-58 to 198
Unclear
Strength and Conditioning 55
-35 to 145
-108
-3 to -212
Possibly Small↘
-163
-61 to -264
Likely Small ↘
Additional
-73
-124 to 269
251
474 to 27
Likely Small ↗
178
396 to 40
Possibly Small↗

12. Conclusions
Training loads undertaken by youth female football players may not reflect that planned by coaches.
In some instances players may partake in more ‘additional’ than ‘planned’ training
Large individual differences in the amount of ‘additional’ activities that athletes engage in.
Compliance of youth female players in providing self-reported training load data is a problematic area – this has been highlighted previously (Malisoux et al ).
Being able to adapt practice week to week seems necessary

13. Practical Applications
Monitoring training load of great importance within youth female players.
Coaches must be aware of individual training schedules to avoid overreaching/overloading players.
Ability to adapt practice is crucial.
When using self-report measures, strategies to increase compliance of youth players should be implemented to avoid lack of adherence.

14. Future research… Strategies to increase athlete compliance.
Prospective studies regarding the participation in additional sports/activities and success at elite level in female football.
The relationship between ‘additional training’ and fitness.
The relationship between ‘additional training’ and injury incidence.

15. References
Borresen, J., & Lambert, M. I. (2009). The quantification of training load, the training response and the effect on performance. Sports Medicine, 39(9),
Brink, M. S., Nederhof, E., Visscher, C., Schmikli, S. L., & Lemmink, K. A. (2010). Monitoring load, recovery, and performance in young elite soccer players. The Journal of Strength & Conditioning Research, 24(3),
Capranica, L. & Millard-Stafford, M.L. (2011) Youth Sport Specialization: How to Manage Competition and Training? International of sports physiology and performance, 6,
Foster, C., Florhaug, J. A., Franklin, J., Gottschall, L., Hrovatin, L. A., Parker, S., Doleshal, P. & Dodge, C. (2001). A new approach to monitoring exercise training. The Journal of Strength & Conditioning Research, 15(1),
Gabbett, T. J., Whyte, D. G., Hartwig, T. B., Wescombe, H., & Naughton, G. A. (2014). The relationship between workloads, physical performance, injury and illness in adolescent male football players. Sports medicine, 44(7),
Hopkins WG., Marshall SW., Batterham AM. & Hanin J. (2009) Progressive statistics for studies in sports medicine and exercise science. Medicine and Science in Sports and Exercise, 41(1), 3-12.
Hopkins, WG (2007) A Spreadsheet for Deriving a Confidence Interval, Mechanistic Inference and Clinical Inference from a P Value. Sportscience, 11,
Malisoux, L., Frisch, A., Urhausen, A., Seil, R., & Theisen, D. (2013). Monitoring of sport participation and injury risk in young athletes. Journal of Science and Medicine in Sport, 16(6),
Malone, J., Di Michele, R., Morgans, R., Burgess, D., Morton, JP. And Drust, B. (2015) Seasonal training-load quantification in elite English premier league soccer players. International journal of sports physiology and performance, 10,
Wrigley, R., Drust, B., Stratton, G., Scott, M., & Gregson, W. (2012). Quantification of the typical weekly in-season training load in elite junior soccer players. Journal of sports sciences, 30(15),

16. Thanks for your attention