# Perceived Training Intensity and Performance Changes Quantification in Judo

Marcus F Agostinho, Antony G Philippe, Gilvan S Marcolino, Ewerton R Pereira, Thierry Busso, Robin Candau, Emerson Franchini

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Marcus F Agostinho, Antony G Philippe, Gilvan S Marcolino, Ewerton R Pereira, Thierry Busso, et al. Perceived Training Intensity and Performance Changes Quantification in Judo. Journal of Strength and Conditioning Research, 2015, 29, pp.1570 - 1577. 10.1519/jsc.0000000000000777. http://04964292

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**Manuscript title:** Training perceived intensity and performance changes quantification in judo

**Brief running head:** Judo training modeling

**Laboratories where the research was conducted :** School of Physical Education and Sport, University of São Paulo, Brazil ; Faculty of Sports Sciences, University of Montpellier, France.

**Authors:** Marcus F. Agostinho¹,²; Antony G. Philippe³; Gilvan S. Marcolino²; Ewerton R. Pereira²; Thierry Busso⁴; Robin B. Candau³; Emerson Franchini¹,³

1 – Martial Arts and Combat Sports Research Group, School of Physical Education and Sport, University of São Paulo, Brazil; 2 – Grêmio Recreativo Barueri and Secretaria de Esportes de Cotia, Brazil; 3 – Faculty of Sports Sciences, UMR 866, University of Montpellier, France; 4 – Laboratoire de Physiologie de l'Exercice, University of Saint-Etienne, France

**Corresponding Author:** Emerson Franchini PhD

Combat Sports and Martial Arts Research Group, School of Physical Education and Sport, University of São Paulo, Brazil, Av. Prof. Mello Morais, 65, Butantã, CEP 05508-900, São Paulo, SP, Brazil.

Phone number: 55 11 3091-2124

emersonfranchini@hotmail.com

Funding disclosure: The authors thank the Brazilian Sports Ministry and the CNPq for the financial support to conduct this study (process number 487302/2013-3).
ABSTRACT

The objective of the present study was to determine the methods of quantification for training and performance which would be the most appropriate for modelling the responses to long-term training in Cadet and Junior judo athletes. For this, 10 young male judo athletes (15.9 ± 1.3 years, 64.9 ± 10.3 kg and 170.8 ± 5.4 cm) competing at a regional/state level volunteered to take part in the study. Data were collected during a two years training period (i.e., 702 days) from January 2011 to December 2012. Their mean training volume was 6.52 ± 0.43 hours per week during the preparatory periods and 4.75 ± 0.49 hours per week during the competitive periods. The followed a training program prescribed by the same coach. The training load was quantified through the session-rating of perceived exertion (RPE) and expressed in arbitrary unit (a.u.). Performance was quantified from five parameters divided in two categories: performance in competition and performance in training. The evaluation of performance in competition was based upon the number of points per level. Performance in training was assessed through four different tests. A physical test battery consisting in a standing long jump, two judo specific tests that were the maximal number of dynamic chin up holding the judogi and the Special Judo Fitness Test. System modeling for describing training adaptations consisted of mathematically relating the training load of the training sessions (system input) to the change in performance (system output). The quality of the fit between training load and performance was similar whether the training load was computed directly from RPE (R² = 0.55 ± 0.18) or from the session-RPE (R² = 0.56 ± 0.18) and significant in 8 athletes over 10, excluding the standing jump from the computation of the training load, leading to a simplest method. Thus, this study represents a first attempt to model training load effects on judo-specific performance and has shown that the best relationships between amounts of training and changes in performance were obtained when training amounts were quantified simply from RPE.
Keywords: combat sports; Training Load; specific tests; Performance; season

INTRODUCTION

Competitive judo is a grappling combat sport in which opponents try to throw, imobilize or submit (via choke or elbow joint-lock technique) each other to win the match, demanding a high technical and tactical development to reach success (18). As the match is composed by high-intensity intermittent actions, with periods of effort near 30s and pauses of approximately 10s, until the end of the time limit (3 to 5 min depending on the age category), physical fitness also plays an important role in this sport (15). In general, adult judo athletes take part in 7 to 12 competitions per year (23) and this model has been introduced to Cadet and Junior judo athletes (24), with high-level competitions as Youth Olympic Games, World, Continental and National Championships being part of the competitive season of young athletes. In high-level judo, hormonal (31) and biochemical control (25) are used to monitor the training responses. However, the resources – especially concerning physical testing - for young judo athletes are smaller than for adult judo athletes. Thus, affordable training monitoring procedures would help to increase the control of training prescription for these athletes, improving performance and avoiding problems related to overtraining, injury and burn-out.

Commonly, the use of rating of perceived exertion (RPE) scales to monitor training responses has gained popularity among coaches, trainers and researchers (11). Among many scales available (11) the approach proposed by Foster et al (12) is one of the most used in combat sports (21, 22, 28, 29, 30, 32, 38, 40). Furthermore, specific physical fitness tests are also recommended to monitor training responses, especially in complex sports where the measurement during the activity is difficulty to conduct (2). The most simple and used specific physical tests to evaluate judo athletes are the Special Judo Fitness Test (15, 36, 35)
and the dynamic chin-up holding the judogi (1, 16). Both tests seem to discriminate judo athletes from different competitive levels (16, 20), and to demand similar metabolic and fatigue profile as that observed during the competitive match (16, 17, 19), although long-term training monitoring using these tests are lacking. Lower-body muscle power has also been reported as important to judo technique execution (43), with vertical jumping performance being a discriminant variable between judo athletes from different competitive levels (44).

One important approach to improve the understanding about training responses and prescription is to model the training process (5), allowing coaches to make better decision making about the stimulus needed to increase athletes' performance. This approach has been used in more controlled aerobic cyclical activities (42), sprint (37) and strength training (8). Sports involving a high technical component, as artistic gymnastics, were only recently investigated (33). However, no long-term training modelling study about combat sports was found. Studies investigating training modelling in sports with high technical-tactical components and dual confrontations are needed to verify the feasibility of such approach in these sports. Additionally, the comparison of different quantification procedures of both training loads and performance in combat sports were not extensively tested. Thus, the objective of the present study was to determine the methods of quantification for training and performance which would be the most appropriate for modelling the responses to long-term training in Cadet and Junior judo athletes.

# METHODS

## Participants

For the present analysis, 10 young male judo athletes competing at a regional/state level took part in the study with mean ± SD age, body mass and height of respectively: 15.9 ± 1.3 years, 64.9 ± 10.3 kg and 170.8 ± 5.4 cm at the beginning of the study. Their mean
training volume was 6.52 ± 0.43 hours per week during the preparatory periods and 4.75 ± 0.49 hours per week during the competitive periods. Their time of judo practice was 6.0 ± 1.9 years, including 2.7 ± 1.1 years registered at the São Paulo State Judo Federation. Data were collected during a two years training period (i.e., 702 days) from January 2011 to December 2012. They followed a training program prescribed by the same coach. The athletes and their parents were fully informed and provided their written consent before entering the investigation. All procedures were approved by the local Ethics Committee.

## Training load quantification

The training load (TL) was quantified through the session-RPE and expressed in arbitrary unit (a.u.). RPE was measured from the Foster session RPE scale (12), which was presented to the athletes 30 minutes after each training session. TL was calculated from two methods, i.e. the product of the duration of the training session in minute and the RPE value and directly from the RPE value provided by the Foster scale. This method was previously validated for combat sports' athletes (21, 22, 28, 29, 30, 32, 40), and the Portuguese version was already used in combat sports' athletes (28, 29 32, 40).

At the beginning of the general preparatory period, the 10 judo athletes were submitted to technical, randori (combat simulation) and strength and conditioning training sessions twice with one week interval. As the training sessions were identical and had the same duration, we used the RPE and training load of each type of session to calculate the reliability of these variables. The following results were found: (a) technical training session (80-min duration) – RPE session 1 = 3.5 ± 0.7 a.u.; Training load session 1 = 276 ± 58 a.u.; RPE session 2 = 4.1 ± 1.1 a.u.; Training load session 2 = 324 ± 85 a.u.; RPE intraclass correlation coefficient = 0.64; RPE limits of agreement = -2.47 to 1.27 (mean difference = -0.60); Training load intraclass correlation coefficient = 0.64; training load limits of agreement
= -197.9 to 101.9 (mean difference = -48.0); (b) *randori* training session (80-min duration) = RPE session 1 = 6.5 ± 1.0 a.u.; Training load session 1 = 516 ± 77 a.u.; RPE session 2 = 6.5 ± 1.2 a.u.; Training load session 2 = 520 ± 100 a.u.; RPE intraclass correlation coefficient = 0.94; RPE limits of agreement = -1.15 to 1.05 (mean difference = 0.05); Training load intraclass correlation coefficient = 0.94; training load limits of agreement = -92.0 to 84.0 (mean difference = -4.0); (c) strength and conditioning training session (60-min duration) - RPE session 1 = 5.2 ± 0.8 a.u.; Training load session 1 = 312 ± 47 a.u.; RPE session 2 = 4.9 ± 0.9 a.u.; Training load session 2 = 294 ± 54 a.u.; RPE intraclass correlation coefficient = 0.79; RPE limits of agreement = -1.13 to 1.73 (mean difference = 0.30); Training load intraclass correlation coefficient = 0.79; training load limits of agreement = -67.8 to 103.8 (mean difference = 18.0). Thus, good reliability values were obtained using this method in the different type of training sessions in young judo athletes.

## Performance quantification

Performance was quantified from five parameters divided in two categories: performance in competition and performance in training. The evaluation of performance in competition was based upon the number of points per level. This value was specifically given by the coach for each competition considering the level (departmental, regional, state) and the number of matches won in each competition. Briefly, points per match won varied from 4 (regional and friendly competitions) to 40 points (national competitions). This was an adaptation of the Cadet and Junior International Ranking List proposed by the International Judo Federation. Performance was assessed through four different tests. A physical test battery consisting in a standing long jump, two judo specific tests that were the maximal number of dynamic chin up holding the judogi (1, 16), the number of throw in the Special Judo Fitness Test (15, 35, 36), and a specific index developed for judo [number of throws /
(HRmax - HRrecovery)] (SJFT index) (15, 17, 19, 36) was used. These tests were performed on Sunday, after a standardized short warm-up.

The standing jump test consisted in three trials with at least one-minute rest in-between. The best value was retained as the performance criterion. The number of dynamic chin-ups test from a single maximal execution was considered. The total number of throws during the three sets (A = 15s, B and C = 30s each, with a 10s rest between consecutive sets) of the Special Judo Fitness Test was registered from a single execution. Athletes were familiarized with all procedures and these tests were described as highly reliable (15, 10, 34). The ICC for the SJFT variables in our laboratory, using a similar sample, were: throws (0.73), HR after the test (0.93), heart rate 1-min after the test (0.89) and index (0.89). The chin-up holding the judogi test had an ICC of 0.98 and limits of agreement of -2.9 to 2.3 rep (mean difference of -0.3 rep). The standing jump test presents a high ICC (0.95) and low coefficient of variation (2.4%) (26). We retained two combinations of these tests in the calculation of the training performance, with the objective to check if training load would related better with three or two tests combination, which would provide the coach the possibility to use a smaller number of test to monitor the athletes' performance. The global training performance (GTP) was determined as follows:

$$GTP = k_a \text{ standing jump} + k_b \text{ number dynamic chin-ups} + k_c \text{ total number of throws}$$

Where $k_a$, $k_b$ and $k_c$ are the respective weighting coefficients for each test. The values of these coefficients are 0.05, 0.45 and 0.50, respectively. They were determined based on time-motion analysis (27) and physiological analysis (13) of judo matches, which have a high demand on repeated all-out efforts as presented in the Special Judo Fitness Test, upper-body strength endurance as evaluated by the chin-up dynamic test and a lower number of actions involving lower-body muscle power during technique application, which was evaluated by
the long standing jump. For the second combination, called simplified global training performance (GTPs) was computed as follows:

$$GTPs = k \text{ number dynamic chin-ups} + k \text{ total number of throws}$$

where k is 0.5 dynamic chin-ups and the total number of throws having an equivalent weighting factor in this simplified performance criterion

## Mathematical modelling

System modelling for describing training adaptations consisted of mathematically relating the training load of the training sessions (system input) to the change in performance (system output) (Figure 1).

For the present study, one mathematical model based upon only one first-order filter effect of training on performance with an impulse response $k_1 e^{-t/\tau_1}$ (7).

Performance $p(t)$ is obtained by adding the basic level of performance $p_0$ to the convolution product of training dose $w(t)$ with the impulse response. The $w(t)$ is considered as a discrete function, i.e., a series of impulse each day, $w_i$ at day $i$. The convolution product becomes a summation in which model performance $\hat{p}^n$ on day $n$ is estimated by mathematical recursion from the series of $w_i$ and is, thus, estimated as follows:

$$\hat{p}^n = p_0 + k_1 \sum_{i=1}^{n-1} w^i \epsilon^{-\frac{i-n}{\tau_1}} \quad (\text{Equation 1})$$

The two parameters of the model were obtained from the fitting of the measured performances and the modelled ones by the minimization of the residuals sum of squares (RSS) between them:

$$RSS = \sum_{n=1}^{N} [p^n - \hat{p}^n]^2$$
(Equation 2)

With *n* taking the *N* values corresponding to the days of measurement of the measured performances.

## Statistical analysis

Selected data were expressed as mean ± SD. The goodness of the fit of performance was assessed with the coefficient of determination (R²). The statistical significance of the model fit was tested for each subject by using analysis of variance of the residuals in accordance with the degree of freedom of the model (df = 2).

Insert Figure 1 near here

## RESULTS

The quality of the fit between training load and performance was similar whether the training load was computed directly from RPE or from the classical method of Foster (i.e. session-RPE) according to which the training load is calculated from the product of RPE by time of training session. Indeed, R²GTP/TL, R²GTP/RPE were, respectively, 0.55 ± 0.18 and 0.56 ± 0.18 and significant in 7 (R²GTP/TL) and 8 (R²GTP/RPE) subjects over 10 (Table 1).

Concerning the quantification of performance variations, no significant relation could be successfully established between the number of points per level in competition and both training load or RPE value (respectively R²Points/TL and R²Points/RPE; p>0.05; Table 1). Thus, we looked for training performance criteria. Both GTP and GTPs provided good correlations with RPE since R²GTP/RPE and R²GTPs/RPE were 0.56 ± 0.18 and 0.54± 0.19, respectively (Table 1), excluding the standing jump from the computation of the training load, leading to a simplest method. Furthermore, no significant correlation between SJFT test and training load could be
evidenced (p>0.05; Table 1). In the present analysis, the optimal combination retained was the correlation between RPE and GTPs. The typical curve of correlation between actual and modeled performance in response to training load is shown in figure 2.

Insert Figure 2 and Table 2 near here

For the 10 athletes, the variation of the mean training load between the first and the second year was not significant (p>0.05) whereas the global training performance increased by 27.09 ± 10.7% during the two years of the studied period (p<0.001).

DISCUSSION

The present study is the first attempt to model the training response of a combat sport.
The most important finding of the present study was the significant relationship between
training load and performance quantification. Surprisingly, (i) the method of Foster developed
(i.e. session-RPE) to quantify training load was less adapted that the simplest method based
only upon the scale (i.e. RPE value) to appreciate the intensity of the training sessions, (ii) the
classical SJFT index was less appropriated to describe the performance variations than the
combination of dynamic chin up holding the judogi and the number of throws during the
SJFT.

Wallace et al (41) used, among other methods, the session-RPE to quantify training load and to verify its relationship with performance in runners and observed a correlation of 0.60 ± 0.10 between modelled and actual performance. As a possible explanation for the moderate relationship found, authors reported that had observed a poor sensitivity to small changes in intensity during moderate-to-hard exercise when using the session-RPE scale, and recommended the substitution of the CR-10 scale by a CR-100 or 6-20 RPE scales as a possible solution to improve the sensibility of training load quantification. The quantification of the training load by the Borg scale takes in consideration the difficulty of the training
session by appreciating both its intensity and duration. The Foster method consisting in multiply both difficulty and duration of the daily training load takes account in reality twice the duration leading to a minimization of the intensity impact on the TL. Consequently, the Foster method was less adapted that the simple quantification by the scale (i.e REP value). Alternative methods based upon heart rate zones (3) or number of movement repetitions weighted by specific coefficients (6, 33) would be also tested in future studies with judo athletes.

Otherwise, in the present study, the best criteria to quantify performance would have been to use directly the results in competition. However, our analysis indicated that the number of points per level did not constitute a good indicator, since no significant correlation could be established between this criteria and the dynamic of training load (Table 1). This result is easily comprehensible, so far as the level of the opponent was not included in this criteria of performance. Except for the international level judo athletes who are ranked by the International Judo Federation, following the same criteria, this aspect is hard to evaluate for other athletes. Alternatively, to consider the results in competitions a technically and tactically homogeneous group of judo athletes taking part in the same competitions should be analyzed, which is quite difficult to investigate and control during such a long period. Thus, the natural alternative solution would have been to use a performance criterium included in the training schedule of the judo athletes. The specific test developed for judo, SJFT index was the first candidate test. Nevertheless, no significant relationship has been evidenced between this index and RPE (p>0.05; Table 1). Interestingly, better results were obtained by simplifying this test to the combination of number of throws to the number of dynamic chin-ups and standing long jump (i.e. GTP). The best relationship between this global training test and the dynamic of training load was obtained. Finally, the simplification of the GTP by excluding the standing long jump (i.e. GTPs) provided similar results (Table 1).
The practice of judo requires tactical, technical and physical development. Modelling training response could lead to an optimization of the training prescription and can improve our knowledge on the effects of training on performance. Nevertheless, the limitations of the modelling approach need to be taken in consideration. The mean R² value obtained in this study (0.54 ± 0.19; n = 10) could be compared to the results obtained for swimmers (0.56 ± 0.06) (39), reflecting the high degree of complexity of the practice of judo especially as this result was obtained from a model taking into account one transfer function, whereas in the study on swimmers, the extended model of proposed by Busso (9) was successfully applied because of a greater number of performance tests (32 ± 3.8).

The most used model is that initially proposed by Banister et al (4) which includes two components: one reflecting the long term adaptation to training (positive effect as for the simpler model used in the present study) and one reflecting the negative effects of training on performance (i.e. fatigue). We have unsuccessfully tested the model of Banister on our data. For example when using RPE value and GTPs for quantifying the training and the performance respectively, the fitting was significant in only 4 subjects for the model of Banister because of the addition of further parameters. It is noteworthy that the df of this model is 4, whereas the df of the model used in this study was only 2. The R² adjusted to the number of model parameters was lower with the model of Banister (0.46 ± 0.26) than the model with only one component used in this study (0.51 ± 0.2; Table 2). Furthermore, a F test on the decrease in residuals according to the increase in model parameters (8) showed that the addition of a negative component in the model gave a significant better fit in only one subject. It was the reason why we have retained the model with only one component in the present study (Table 2).

A possible explanation of the unsuccessful application of the Banister model could be that the measures of performance were to space out. It suggests that for sports involving a
highly complex coordination and both technical and physical capacities, performance needs to be measured more frequently if the negative component of training (i.e. fatigue) wants to be evidenced as it has been shown in the study of Sanchez et al (33) where 33 performance measures have been collected in 3 month in elite gymnasts. Thus, by including several times a week after the training session the simple testing procedure including number of throws and the number of dynamic chin-ups, it would be possible to reduce the average elapse time between two measures of performance (i.e. 48.5 ± 1.7 days in our study).

The training load stayed significantly constant between the two years of the experiment, suggesting that the perception of the training session intensity expressed by the RPE was reliable to estimate training load because all the athletes had the same coach and a constant training program over the two years of the experiment. However, it is important to emphasize that the global training performance increased by 27.09 ± 10.7% during the two years of the studied period. This can be explained by the fact that the performance test battery is based upon both physical and technical development, especially the number of throws in the Special Judo Fitness Test. Thus, a technical improvement may have resulted in increased performance independently from the training load. Moreover, the use of the TRIMPS method of Banister on the whole session including the recovery period could constitute a solution in so far as the anaerobic component that slip from measure of the heart rate during the exercise becomes perceptible if the recovery HR is also taken into account. Nevertheless, the session-RPE method for training load quantification can be considered as a good indicator for most of the athletes, as the training load stayed constant.

The aim of the present study was to determine if the session-RPE training quantification method was appropriate to modelling the responses to long-term training in Cadet and Junior judo athletes. In conclusion, this study has shown that the best relationships between amounts of training and changes in performance were obtained when training
amounts were quantified simply from RPE. Furthermore, the best results were obtained when the performance was quantified using a combined criterion that includes maximal specific movement repetitions of chin-ups gripping the judogi and throws during the SJFT, excluding the standing jump test so far as including this test in the global training performance did not enhance the correlation coefficient. Based on this preliminary study, it is now possible to move to a systematic approach of training response especially with a frequent monitoring of performance based on simple tests included in the training program of judo athletes. The goal is to attain a reliable description of the training process in complex disciplines such as combat sports in order to have access to models to simulate training strategies and to optimize the planning of future training.

## PRACTICAL APPLICATIONS

The proper training monitoring is a key component to understand training adaptation and improve training prescription. However, although judo-specific tests have been developed the comprehension of the impact of training load on performance change in these tests thoroughly the season was not investigated previously. The results of the present study demonstrated that simply monitoring the rating of perceived exertion can predict more than 50% of the variance on performance changes in judo-specific tests (i.e., gripping strength endurance performance and number of throws during a high-intensity intermittent test). Thus, coaches should include the monitoring of rating of perceived exertion in each training session to better understand the variation on judo-related performance. Additionally, this approach can be improved over the years and specific decision-making concerning the proper stimulus for each athlete can be developed, which can guarantee the application of the individualization training principle.

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<figure><img src="image_2.png" /></figure>
Figure 1 – Tested correlations between performances and training loads

Black lines are the correlations between performances and training load. Grey lines are the correlations between performances and RPE value.

GTP, Global Training Performance; GTPs, simplified Global training performance; SJFT index, Special Judo Fitness Test index; Duration is expressed in minutes.

Figure 2 - Application of the model on subject 2. (A) Modelled (grey line) and actual (black dots) performance in response to training load. (B) Evolution of training load, quantified directly from the RPE value over the 702 days of experiment.
<table><caption>Table 1 - Individual coefficients of determination between actual and modeled performance</caption><thead><tr><th></th><th>Athlete 1</th><th>Athlete 2</th><th>Athlete 3</th><th>Athlete 4</th><th>Athlete 5</th><th>Athlete 6</th><th>Athlete 7</th><th>Athlete 8</th><th>Athlete 9</th><th>Athlete 10</th><th>Mean ± SD</th></tr></thead><tbody><tr><td>R<sup>2</sup><sub>GTP/TL</sub></td><td>0.79*</td><td>0.75*</td><td>0.70*</td><td>0.66*</td><td>0.57*</td><td>0.51*</td><td>0.48*</td><td>0.37</td><td>0.35</td><td>0.30</td><td>0.55 ± 0.18</td></tr><tr><td>R<sup>2</sup><sub>GTPs/TL</sub></td><td>0.79*</td><td>0.76*</td><td>0.71*</td><td>0.65*</td><td>0.56*</td><td>0.48*</td><td>0.48*</td><td>0.36</td><td>0.35</td><td>0.30</td><td>0.54 ± 0.18</td></tr><tr><td>R<sup>2</sup><sub>GTP/RPE</sub></td><td>0.80*</td><td>0.79*</td><td>0.7*</td><td>0.67*</td><td>0.58*</td><td>0.48*</td><td>0.47*</td><td>0.42*</td><td>37.8</td><td>0.33</td><td>0.56 ± 0.18</td></tr><tr><td>R<sup>2</sup><sub>GTPs/RPE</sub></td><td>0.79*</td><td>0.79*</td><td>0.7*</td><td>0.67*</td><td>0.58*</td><td>0.44*</td><td>0.47*</td><td>0.41*</td><td>0.34</td><td>0.26</td><td>0.54 ± 0.19</td></tr><tr><td>R<sup>2</sup><sub>Points/RPE</sub></td><td>0.04</td><td>0.002</td><td>0.03</td><td>0.01</td><td>0.11</td><td>0.07</td><td>0.05</td><td>0.06</td><td>0.02</td><td>0.13</td><td>0.05 ± 0.04</td></tr><tr><td>R<sup>2</sup><sub>Points/TL</sub></td><td>0.02</td><td>0.003</td><td>0.23</td><td>0.001</td><td>0.01</td><td>0.05</td><td>0.06</td><td>0.04</td><td>0.05</td><td>0.11</td><td>0.06 ± 0.07</td></tr><tr><td>R<sup>2</sup><sub>SJFT index/RPE</sub></td><td>0.31</td><td>0.01</td><td>0.06</td><td>0.49</td><td>0.33</td><td>0.01</td><td>0.04</td><td>0.25</td><td>0.07</td><td>0.05</td><td>0.10 ± 0.12</td></tr></tbody></table>

Note: Individual values and mean ± standard deviation of different coefficient of determination expressed without unit. R<sup>2</sup><sub>GTP/TL</sub>, R<sup>2</sup><sub>GTPs/TL</sub>, R<sup>2</sup><sub>GTP/RPE</sub>, R<sup>2</sup><sub>GTPs/RPE</sub>, R<sup>2</sup><sub>Points/TL</sub>, R<sup>2</sup><sub>Points/RPE</sub> and R<sup>2</sup><sub>SJFT/RPE</sub> have been obtained by computing Model-1. * P < 0.05
<table><caption>Table 2 - Indicators of goodness-of-fit of performance for various systems models of training effects</caption><thead><tr><th rowspan="2">Subject No.</th><th rowspan="2">N</th><th colspan="2">Model-1 df = 2</th><th colspan="2">Model-2 df = 4</th><th colspan="2">R2 gain between model 2 and 1</th></tr><tr><th>R<sup>2</sup><sub>Model-1</sub></th><th>Adj R2</th><th>R<sup>2</sup><sub>Model-2</sub></th><th>Adj R2</th><th>f-ratio</th><th>P-value</th></tr></thead><tbody><tr><td>1</td><td>15</td><td>0.79</td><td>0.76†</td><td>0.82</td><td>0.75†</td><td>0.833</td><td>0.463</td></tr><tr><td>2</td><td>15</td><td>0.79</td><td>0.76†</td><td>0.79</td><td>0.71†</td><td>0</td><td>1</td></tr><tr><td>3</td><td>15</td><td>0.70</td><td>0.65†</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>4</td><td>15</td><td>0.67</td><td>0.62†</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>5</td><td>15</td><td>0.58</td><td>0.51†</td><td>0.65</td><td>0.51†</td><td>1,000</td><td>0.402</td></tr><tr><td>6</td><td>15</td><td>0.44</td><td>0.35†</td><td>0.46</td><td>0.24</td><td>0.185</td><td>0.834</td></tr><tr><td>7</td><td>15</td><td>0.47</td><td>0.38†</td><td>0.77</td><td>0.68†</td><td>6.522</td><td>0.015*</td></tr><tr><td>8</td><td>15</td><td>0.41</td><td>0.31†</td><td>0.45</td><td>0.23</td><td>0.364</td><td>0.704</td></tr><tr><td>9</td><td>15</td><td>0.34</td><td>0.23</td><td>0.35</td><td>0.09</td><td>0.077</td><td>0.927</td></tr><tr><td>10</td><td>15</td><td>0.26</td><td>0.14</td><td>0.44</td><td>0.22</td><td>1.607</td><td>0.248</td></tr></tbody></table>
Mean ± SD 0.55 ± 0.19 0.51 ± 0.2 0.59 ± 0.19 0.46 ± 0.26

Note: Model-1, model using one first-order component; Model-2, model using two first-order components; N, number of measurements of performance; Adj.R2, adjusted coefficient of determination. Significance of the fit: † P < 0.05; Statistical difference from Model-1Comp: * P < 0.05
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(A)

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(B)

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