Planning and Monitoring Training Loads During the Competition Phase in Team SportsVincent G. KellySouth Sydney Rabitohs, Redfern, Australia

Aaron J. Coutts, PhDSchool of Leisure, Sport, and Tourism, University of Technology, Sydney, Australia

© National Strength and Conditioning AssociationVolume 29, Number 4, pages 32–37

Keywords: training loads; team sports; periodization

Introduction

Periodization offers a frameworkfor planned and systematic varia-tion of training parameters with

the goal of optimizing training adapta-tions specific to a particular sport (9). Inteam sports, the typical model of peri-odization will include the followingphases: general preparation, specificpreparation, precompetition, and com-

petition (5). A common problem forcoaches is determining the appropriatetraining loads to prescribe during thecompetition phase of the season. Factorssuch as the quality of the opposition, thenumber of training days between match-es and any travel associated with playingaway games all influence the between-match periodization of training loads.We suggest that a combination of thesefactors can be used to guide planning ofthe between-match weekly trainingloads. For example, a team preparing fora difficult match (i.e., strong opposi-tion, little preparation time, and signifi-cant away travel) may plan a light train-ing week so that any residual fatigue isminimized. Conversely, a home gameagainst weaker opposition with severaldays between games may provide an op-portunity to have an increased trainingload to improve the player’s fitness lev-els.

Cormack (2) developed a periodizationmodel for an Australian Rules FootballLeague team based on the number oftraining days between matches and theeffect of interstate travel. In this previ-ous paper, several examples of how thetraining week (microcycle) can be ma-nipulated to allow optimal recovery,while also allowing the development

and refinement of the ongoing techni-cal and tactical requirements for teamsports were provided (2). It was alsosuggested that the success of an in-sea-son periodization plan is affected byseveral factors, including the skills /technical coaches’ understanding ofthe training process, the measurementand prescription of the volume and in-tensity of skill training sessions, andthe balance between training, rest, andrecovery.

Quite often the challenge of thestrength and conditioning coach is toeducate the skills coach and athletes onthe intricacies of a detailed periodizedplan. The tendency of many skillscoaches is to react to the situation orcoach using intuition rather than fol-lowing a specific plan (2). Further-more, it has also been shown that thetraining loads that have been plannedto be completed by the coach are oftenpoorly executed by the athlete (8). Forexample, Foster et al. (8) reported thatathletes often complete high-intensitytraining sessions at lower intensitiesthan coaches intend and they oftencomplete lower-intensity recovery ses-sions at higher intensities than initiallyplanned. These findings suggest that ei-ther many coaches are not implement-

s u m m a r y

The training and match demands in

the competition phase in team sport

can vary dramatically depending on

the quality of opposition, the num-

ber of days between games, and

match location. The simple model

described in this article can be used

to predict the match difficulty and

guide the prescription of future

training loads during the in-season

training phase in team sports.

32 August 2007 • Strength and Conditioning Journal

ing their training programs accordingto intention or that athletes are unableto control their training intensity ac-cording to coaches’ instructions. Re-gardless of the cause, this mismatch inperception of training between athletesand their coaches may result in subop-timal performance. Due to this, itseems that it is necessary to develop atraining monitoring system that allowscoaches to plan and monitor the work-loads undertaken by their athletes.From a practical perspective, this sys-tem should also be simple to under-stand and easy to implement in a teamenvironment.

The session–rating of perceived exertion(RPE) model developed by Foster et al.(7) is a simple system for strength andconditioning coaches to monitor theload of several different modalities oftraining (technical, tactical, endurance,speed, and strength). With this system,players are required to provide an RPEfor each exercise session that is thenmultiplied by the training session dura-tion (min) to determine training load.The simplicity of this system makes iteffective for quantifying training load inteam sports.

The purpose of this paper is to offer anexample of a simple system that can beused to guide in-season training loads inteam sports. This paper will outline amodel that can be used to predict matchdifficulty and also use the session-RPEsystem to guide the training process inteam sports. We will also present an ex-ample from a semi-professional rugbyunion team as a case study to show howtraining loads and match difficulties canbe monitored throughout the season toguide the coaches with their training pe-riodization.

Overview of the SystemWhen developing an in-season (i.e.,competition phase) training program itis important to have a simple system thatallows the coach to predict the difficultyof each match, plan the weekly training

load accordingly, and review the pro-gram on a regular basis. Figure 1 illus-trates how this simple system works.

• Step 1. Predict the difficulty of eachmatch.

• Step 2. Plan the weekly trainingload.

• Step 3. Monitor the actual weeklytraining load.

• Step 4. Review and return to step 1.

Each step in this system is discussed indetail below.

Predicting Match DifficultyBefore the commencement of the seasonthe coaching staff should determine thelevel of difficulty for each match of theseason by calculating the sum of 3 fac-tors. These factors are level of opposi-tion, training days between matches,and match location.

Level of opposition has the greatest ef-fect on match difficulty followed bytraining days between matches andmatch location. Therefore, the score forlevel of opposition is greater than thescore for training days between matcheswhich is greater than the score for matchlocation.

Level of OppositionPrior to the commencement of the com-petition, each team, including the teamyou are working with, is ranked fromstrongest to weakest and given a score

based on their rank. For example, thebest team in the competition is rankedfirst and receives the highest level of op-position score, and the team ranked lastin the competition receives the lowestscore. During the competition phase,the coaches reassess the opposition atthe start of each week, and a new rank isdetermined. The new rank is based onthe results of the previous round. This inturn may or may not affect the originalmatch difficulty figure for that week.

Table 1 shows an example of scores forlevel of opposition in a 10-team compe-tition. These scores can be adjusted fordifferent numbers of teams in the com-petition.

Training Days Between MatchesIt is common in team sports for thematches to be conducted over the week-end (i.e., from Friday night until Mon-day night). The number of days betweenmatches may vary between 4 and 8 days,which may have a significant impact onthe team’s preparation. A sliding scalehas been prepared to allow for these dif-ferences, and scores are allocated foreach different number of days betweenmatches (Table 2).

Match LocationMatch location can have an impact onthe weekly training load. Scores are allo-cated for home and away matches. Addi-tional points can be added if the team isrequired to travel significant distances

33August 2007 • Strength and Conditioning Journal

Figure 1. A diagrammatic representation of the system.

and spend time away from their usualenvironment (Table 3).

Match Difficulty CalculationsAfter the scores for each factor are de-termined each opposition team is givena total score. This score is used to repre-sent how difficult the coaches predictthe matches may be. This task should beperformed at the start of each newweek.

Table 4 shows an example of the scoresfor each factor and the total score repre-senting match difficulty as calculated atthe start of the season. In this example,there are 10 teams in the competition,

between 4 and 8 days between matches,and 1 “away” match with travel.

Training LoadIntensity and VolumeThe training intensity for each session iscalculated using the modified CategoryRatio-10 RPE scale described by Fosteret al. (7). Players are asked to rate howdifficult the entire session is on a scale of1 to 10 using a modified RPE scale(Table 5). These measures are taken afterapproximately 30 min of the completionof training. We have found that athletesof all levels can use the RPE scale reli-ably once they have been anchored usingstandard methods (1). The training vol-ume is quantified using total trainingtime (min).

Load The volume (minutes) is multiplied bythe global intensity (RPE) to determinetraining load for all individual sessions. Asimple example where training sessionsare held twice weekly is shown in Table 6,the training loads from these sessions areadded to provide the weekly training load.

In-Season Match DifficultyIn-season periodization in team sportis an ongoing process and the level of

opposition is a key factor in estimatingmatch difficulty. As the season pro-gresses, teams performing better orworse than expected have an impacton the match difficulty. Figure 2shows a case study of a season wherethe actual match difficulty was greaterthan the predicted match difficulty.Interestingly, this pattern appeared to-wards the end of the season. In this ex-ample, the opposition performed bet-ter throughout the season than thecoaching staff had initially expected.At the start of the season, the coacheshad initially planned for these matchesto be less difficult; however, by usingthe current model the coaches wereable to reassess the level of oppositionon a weekly basis. The readjustedmatch difficulty scores were used bythe coaches and strength and condi-tioning staff to adjust their trainingplans to ensure optimal preparationfor each match.

In-Season Training LoadPredicted Versus Actual TrainingLoadThe monitoring of training loads isimportant for coaches to determine ifthey are implementing training stressaccording to their plan (3, 4, 8, 11).

Table 3Categories and Scores for

Match Location

Factor Score

Away with travel 3

Away 2

Home 1

34 August 2007 • Strength and Conditioning Journal

Table 2 An Example of Categories and

Scores for 4–8 Training Days Between Matches

Factor Score

4 days 8

5 days 6

6 days 4

7 days 2

8 days 1

Table 1 An Example of Categories and

Scores for Level of Opposition in a10-Team Competition

Factor Score

Top 4 teams 12–9

Middle 3 teams 8–6

Bottom 3 teams 5–3

Figure 2. An example of predicted and actual match difficulty throughout a season.

Recent studies have shown that thesession-RPE method compares favor-ably with more complicated and inva-sive methods for monitoring trainingloads in athletes (4, 6, 10, 12). For ex-ample, several studies have shown asignificant relationship between ses-sion-RPE training load and variousheart rate–based methods of monitor-ing training load in team sports suchas basketball (6), soccer (10), andrugby league (4). Moreover, Sweet etal. (12) also reported a high agree-ment between session-RPE andweightlifted (% 1 repetition maxi-mum [1-RM]) during a range of resis-tance training sessions. Due to the fa-vorable relationships between thesemethods for quantifying training invarious exercises, we suggest that themodel presented in this paper is a sim-ple and inexpensive system thatstrength and conditioning coachescan use to monitor and guide thetraining process.

Using this method, the session-RPEsystem is applied in a team-sport set-ting and training loads are both moni-tored and predicted in an attempt tobetter guide in-season training. Eachweek the staff can examine the amount

of training performed in the previousweek combined with the new matchdifficulty score for the following week.They can then reassess their trainingplan to allow for any changes to theoriginal plan. Figure 3 provides an ex-ample of planned and actual trainingloads throughout a season. In thiscase, the actual training load wasgreater than the predicted load in the

week of Game 1. The coaches adjustedtheir training sessions for the follow-ing week to account for this differenceand the actual training load was slight-ly less than what had originally beenplanned.

Table 5The Modified Rating of Perceived

Exertion Scale Used for Athletes toClassify Their Perceived Intensity of

Each Training Session (7)

Rating Descriptor

0 Rest

1 Very Easy

2 Easy

3 Moderate

4 Somewhat Hard

5 Hard

6

7 Very Hard

8

9

10 Maximal

Table 4An Example of Scores for Each Factor and Total Match Difficulty for Each Team

Opposition Level Days Location Total

Team 1 11 4 1 16

Team 2 3 4 2 9

Team 3 12 6 3 21

Team 4 10 2 1 13

Team 5 7 4 1 12

Team 6 5 8 2 15

Team 7 4 1 2 7

Team 8 6 4 1 11

Team 9 8 4 2 14

Note: There is no score of 9 under level of opposition. In this example, the coaches rated theirown team fourth at the start of the season, which represented a score of 9 points.

35August 2007 • Strength and Conditioning Journal

Figure 3. An example of predicted and actual weekly training loads throughout aseason.

Match Difficulty and TrainingLoadsThe predicted level of difficulty of amatch is used to determine the trainingload for the week. In general, the train-ing load will be decreased for a moredifficult match and increased for a lessdifficult match. Figure 4 shows an ex-ample of this relationship. In this ex-ample, the predicted match difficulty islow for the second game of the season;therefore, a week of greater trainingload is planned. Conversely, the thirdgame of the year has a high predictedmatch difficulty, and a reduced trainingload is planned for that week. It is in-teresting to observe that this approachto training resulted in undulating, non-linear periodization of training loads(see Figure 4). This periodization

model is in agreement with the previ-ous literature that suggests that nonlin-ear, undulating training models aremore common during the in-season pe-riod in team sports (4, 7).

Often skills/technical coaches have atendency to prescribe longer trainingsessions in the lead-up to importantmatches, believing the players will bene-fit from the extra training. Using thismethod, the strength and conditioningcoach can provide feedback to the skillscoach to demonstrate the effect extratraining may have on recovery and per-formance. This will ensure the teammaintains optimal fitness levels leadinginto the finals matches and, therefore,allow for a taper-like reduction in train-ing loads during the finals period.

ConclusionSeveral factors influence the periodiza-tion of training loads during the com-petition phase in team sports. The sim-ple method described in this article canbe used to predict the difficulty of eachmatch by examining the level of theopposition, the number of days be-tween matches and the match location.Using this method in a variety of teamsport competitions will provide thestrength and conditioning coach with agreater understanding of in-season pe-riodization. In order to achieve this, wesuggest that session-RPE should bemonitored in all training sessions in-cluding strength-training sessions,conditioning sessions, and tactical andtechnical training sessions to provide aglobal rating of training load. Thestrength and conditioning coach canuse this information to determine thein-season training loads and monitorthese factors throughout the season toensure the team’s optimal physicalpreparation. ♦

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Table 6 An Example of Total Training Load for a Typical Week In-Season

Day Average RPESession duration

(min) Training load

Tuesday 4.9 120 589

Thursday 4.7 105 496

Weekly Load 1,086

RPE = rating of perceived exertion.

Figure 4. Match difficulty versus total weekly training loads. AU = arbitrary units.

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Vincent Kelly is the Head Strength andConditioning Coach for the South SydneyRabitohs in the National Rugby League.

Aaron Coutts is a Senior Lecturer in Sportand Exercise Science at the School ofLeisure, Sport, and Tourism within theUniversity of Technology, Sydney.

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Coutts

Kelly