Data Visualization for Dynamic Strength Index: A Qualitative

Approach for Enhanced Interpretation and Decision-Making

Zane Šmite

, Artūrs Paiķis

and and Līga Plakane

Faculty of Medicine and Life Sciences, University of Latvia, Jelgavas Street 1, Riga, Latvia

Keywords: Personalized Training, Testing, Data Visualization, Strength, Power.

Abstract: Dynamic strength index (DSI) serves as an important metric for assessing the balance between athletes

maximal and ballistic strength. However, its interpretation can be limited if the effects of both isometric and

ballistic components are not considered. The aim of this study is to develop a qualitative approach for

interpreting DSI through data visualization, providing strength and conditioning coaches’ clearer insights that

may guide more effective training recommendations. Thirty male college-level basketball athletes performed

countermovement jumps (CMJ) and isometric mid-thigh pulls (IMTP) as a part of late-season testing. The

peak IMTP force normalized to body mass was 39.02±3.57 N/kg, while peak CMJ force was 25.57±2.92 N/kg.

The mean DSI was 0.66±0.09, where 2 athletes attained a DSI ≥0.80, 19 athletes between 0.60 and 0.80, and

9 athletes ≤0.60, corresponding to recommendations for maximal strength, concurrent, and ballistic training,

respectively. T-score adjustments, used to categorize athletes based on maximal strength, resulted in the

reclassification of 5 athletes from the concurrent training group to the maximal strength development group,

and 5 athletes from the ballistic training group to the concurrent training group. Visualizing the DSI in a

scatter plot, alongside T-score performance bands from the IMTP, allows for better evaluation of athletes'

weaknesses and may guide more effective strength training recommendations.

1 INTRODUCTION

Basketball is defined by many high-intensity

neuromuscular activities, such as sprinting, jumping,

and rapid changes in direction, with frequent physical

contacts between athletes (Stojanović et al., 2018,

Petway et al., 2020, Wellm et al., 2024).

Consequently, developing significant strength and

power is essential for optimal athletic performance in

basketball.

Assessing an athletes’ strength capacity through

targeted testing can provide valuable insights into

neuromuscular function, enabling coaches to develop

more personalized and effective training

recommendations (McGuigan et al., 2013, Lockie et

al., 2018, Morrison et al., 2022). Dynamic strength

index (DSI) has been proposed as a promising method

for balance evaluation between athletes maximal and

ballistic strength capabilities (Sheppard et al., 2011,

McMahon et al., 2017, Pleša et al., 2024). It is

calculated as the ratio between an athlete's peak

https://orcid.org/0000-0002-8316-8807

https://orcid.org/0009-0007-5848-1854

https://orcid.org/0009-0007-2777-1154

ballistic force (e.g., countermovement jump (CMJ),

squat jump) and peak isometric force (e.g., isometric

mid-thigh pull (IMTP), isometric squat) (Comfort et

al., 2018). Generally, a low DSI (≤0.6) suggests a

focus on ballistic training to improve power output,

while a high DSI (≥0.8) indicates a need for maximal

strength training. For intermediate DSI values (0.6 –

0.8), a concurrent training approach that incorporates

both ballistic and strength training is recommended

(Sheppard et al., 2011).

However, when interpreting DSI, many studies

recommend considering the effects of both isometric

and ballistic components. DSI alone should not be

used to track training-induced progress, as

simultaneously increasing both components may not

significantly change DSI but would still indicate

positive training adaptations (Bishop et al., 2023,

Pleša et al., 2023, 2024). Additionally, for athletes

with poor relative strength, prioritizing the

development of maximal strength might be more

beneficial than focusing solely on achieving a specific

Šmite, Z., PaiÄ ˚uis, A. and Plakane, L.

Data Visualization for Dynamic Strength Index: A Qualitative Approach for Enhanced Interpretation and Decision-Making.

DOI: 10.5220/0013069600003828

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 12th Inter national Conference on Sport Sciences Research and Technology Support (icSPORTS 2024), pages 271-276

ISBN: 978-989-758-719-1; ISSN: 2184-3201

271

DSI value (Cormie et al., 2010, Suchomel et al.,

2020). Therefore, it is essential for coaches to

consider the broader context of different strength

quality development when using DSI to guide

training recommendations.

In sport science, decision support systems that

incorporate data visualization are gaining increasing

popularity due to their ability to simplify complex

data and improve information delivery. By converting

raw performance data into visually accessible

formats, these systems allow for quicker, more

comprehensive analysis and support better informed

decision-making (McGuigan et al., 2013, Calder et

al., 2015, Lockie et al., 2018, Torres-Ronda et al.,

2024).

In addition, normalized scores like z-scores and t-

scores are used to standardize data and create

benchmarks, allowing for meaningful comparisons

among individuals within a group (McGuigan et al.,

2013, Lockie et al., 2018, Turner et al., 2019,

McMahon et al., 2022). A z-score shows how many

standard deviations a data point is from the group

mean, which helps evaluate an individual's

performance relative to the group norm. T-score is a

transformed z-score, calculated by multiplying the z-

score by 10 and adding 50, resulting in a more user-

friendly format ranging from 0 to 100 (Turner et al.,

2019, McMahon et al., 2022). By visualizing athletes'

normalized score data, coaches can effectively assess

each athletes’ performance in comparison to their

teammates and identify individual strengths and

weaknesses (McGuigan et al., 2013, Lockie et al.,

2018, Turner et al., 2019, McMahon et al., 2022).

The aim of this study is to develop a qualitative

approach for interpreting DSI through data

visualization, providing strength and conditioning

coaches’ clearer insights that may guide more

effective training recommendations.

2 METHODS

2.1 Participants

This cross-sectional study involved thirty male

college-level basketball athletes (age 19.5 ± 2.4 years;

height 1.94 ± 0.08 m; body mass 87.2 ± 9.5 kg) who

participated in a single testing session as part of their

late-season evaluation. To ensure recovery, the

testing was done one day after a rest day. Written

informed consent was obtained from all participants

prior to testing. The study was approved by the

Research Ethics Committee of the Faculty of

Biology and the Faculty of Geography and Earth

Sciences at the University of Latvia (Nr. 18-29/30)

and adhered to the ethical guidelines outlined in the

World Medical Association’s Declaration of

Helsinki.

2.2 Testing

After arriving at the gym, each athlete completed a

standardized warm-up consisting of activation

exercises and dynamic lower body stretching.

Following a brief rest, athletes performed two sets

of three maximal-effort countermovement jumps,

with a 20-s rest between each jump and 3-min rest

between sets. Athletes were instructed to place their

hands on their hips and maintain this position

throughout the entire movement. They were asked to

perform a countermovement to a self-selected depth

and then jump as high and as fast as possible.

To prevent potential post-activation performance

enhancement effect of the IMTP on the CMJ

(Blazevich et al. 2019), the IMTP test was conducted

after the CMJ. IMTP was conducted with participants

positioned at a knee joint angle of 125 – 145 degrees

and a hip joint angle of 140 – 150 degrees, resulting

in the barbell being positioned at approximately mid-

thigh level. Once the bar height was established,

athletes’ hands were strapped to the bar using

standard lifting straps. Before the main test,

participants completed two warm-up pulls at 50% and

75% of their perceived maximum effort followed by

three maximal effort IMTPs, with each trial separated

by 1-min rest period. If peak force varied by >250 N,

the trial was repeated. Athletes were instructed to pull

the bar as hard as possible while also pushing their

feet into the force plates for 3 – 5 s and received

strong verbal encouragement throughout the test

(Comfort et al., 2019).

All IMTP and CMJ were performed on dual force

plates (MuscleLab, Ergotest Innovation AS, Norway)

set at sampling rate of 1000 Hz and data was stored

within the MuscleLab Professional Software, which

enables immediate and reliable calculation of force-

time variables. The best value from the test trials was

used for subsequent data analysis. The DSI was

calculated by dividing peak CMJ force by peak IMTP

force. All athletes were familiar with the tests, having

completed them in previous sessions, ensuring

consistency and reducing variability in performance.

2.3 Statistical Analyses

Data are presented as mean ± SD. The normal

distribution of IMTP and CMJ metrics was confirmed

using the Shapiro-Wilk test. To establish benchmarks

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for the IMTP data, T-score performance bands using

IMTP peak force normalized to body mass were

created. These T-score bands were defined using 1

SD around the mean and assigned the following

qualitative descriptions: ≤40 (poor), >40 – ≤60

(average), and >60 (good) (Robertson et al., 2017,

McMahon et al., 2022).

The calculation of T-score performance bands

was done using Microsoft Excel spreadsheet

(McMahon et al., 2022). The plots have been created

using Rstudio (R Core Team, 2020) and the ggplot2

(Wickham, 2016) and ggExtra (Attali & Baker, 2023)

packages.

3 RESULTS

During the late season, the peak force for the college-

level men’s basketball athletes in the IMTP test was

3397.34 ± 454.82 N, while the peak force in the CMJ

test was 2223.09 ± 296.16 N. When normalized to

body mass, the peak IMTP force was 39.02 ± 3.57

N/kg and 25.57 ± 2.92 N/kg in the CMJ test.

The mean DSI across all athletes was 0.66 ± 0.09.

Two athletes recorded a DSI ≥0.80, 19 athletes had a

DSI between 0.60 and 0.80, and 9 athletes had a DSI

≤0.60, with corresponding training recommendations

for maximal strength, concurrent, and ballistic

training, respectively (Figure 1).

Figure 1: Visualization of ballistic and maximal strength

with dynamic strength index recommendations shown as

background colors.

Based on T-score performance bands, the

following benchmarks for peak IMTP force

normalized to body mass were established: poor

maximal strength ≤35.45 N/kg, average maximal

strength >35.45 – ≤42.60 N/kg, and good maximal

strength >42.60 N/kg.

Table 1: T-score performance bands for IMTP peak force

normalized to body mass.

Description T-score

Peak IMTP force,

N/kg

Good >60 >42.60

Average >40 – ≤60 >35.45 – ≤42.60

Poor ≤40 ≤35.45

If an athletes' IMTP peak force normalized to

body mass was classified as poor, the original DSI

training recommendation was adjusted to prioritize

enhancing maximal strength. For athletes with

average IMTP peak force, recommendations were

adjusted so that incorporating some degree of

maximal strength training remained necessary

(Figure 2).

Figure 2: Dynamic strength index recommendation

adjustment to maximal strength performance bands.

After considering the T-score based maximal

strength performance band adjustments, a

reassessment of the athletes' training

recommendation group assignments was conducted.

As a result, 5 additional athletes from the DSI-

prescribed concurrent training group were moved to

the group focused on maximal strength

improvements, and 5 athletes from the ballistic

training group to the concurrent training group

(Figure 3).

Data Visualization for Dynamic Strength Index: A Qualitative Approach for Enhanced Interpretation and Decision-Making

273

Figure 3: Visualization of ballistic and maximal strength

with dynamic strength index recommendations adjusted for

maximal strength performance bands.

4 DISCUSSION

The athletes of current study have higher peak IMTP

forces compared to those reported in previous

research on basketball players (Thomas et al., 2017;

Pleša et al., 2024). However, peak CMJ force are

similar to other studies with male basketball players

(Thomas et al, 2017, Pleša et al., 2023, 2024).

The DSI values in previous research were higher

than those recorded in this study (Thomas et al., 2017;

Pleša et al., 2024). This discrepancy could be

attributed to seasonal fluctuations in DSI (Pleša et al.,

2023), as the current data were collected during the

late season. Further, differences may be caused due

to the fact that in the current research basketball

player resistance training still emphasized maximal

strength development, which may have helped to

maintain maximal strength capacity. However,

accumulated fatigue over the season might have

contributed to lower peak CMJ force values, as CMJ

has been shown to be sensitive to detecting fatigue

over time (Wu etl al. 2019, Alba-Jiménez et al. 2022).

Given the association between maximal strength

and power, traditional training periodization models

emphasize the development of maximal strength

before transitioning into power-oriented training

(Taber et al., 2016, Stone et al., 2021). This strong

foundation helps athletes’ transition more effectively

to high-velocity, power focused training. By

employing T-score derived IMTP performance

bands, this visualization assists in identifying athletes

who have suboptimal maximal strength and may

benefit from further strength development, even when

the DSI indicates otherwise. For example, the current

data of college-level basketball players shows that five

athletes from the ballistic training group, initially

assigned based on their DSI, could be reassigned to the

concurrent training group, as they have not yet

achieved good maximal strength. Additionally, five

athletes in the concurrent training group exhibited poor

maximal strength, suggesting that their primary focus

should remain on maximal strength development. If

ballistic training is included concurrently, the emphasis

should still be on building a strong foundation of

maximal strength to ensure balanced progress and

optimal performance outcomes.

Preparing and visualizing athlete testing data is

essential for clearly communicating insights to

stakeholders and enhancing decision-making. By

incorporating details on how peak IMTP force

changes independently from peak CMJ force and

adding maximal strength performance bands, this

approach can help coaches to better identify

weaknesses in both ballistic and maximal strength.

This provides a clearer understanding than relying on

numeric DSI data alone, leading to more informed

and targeted training decisions.

However, it should be noted that to establish

accurate benchmarks, a larger sample size than used

in this study is necessary (Turner et al., 2019).

Additionally, benchmarks should ideally be

calculated when athletes are at peak performance,

unaffected by the accumulated fatigue of the season.

Therefore, further studies are needed to develop

precise benchmarks that can be used to create

accurate visualizations for basketball players, aiding

in more informed training recommendations.

Nevertheless, this approach holds promise, as it

overcomes some of the limitations of relying just on

a single numeric DSI value.

5 CONCLUSIONS

Visualizing the DSI in a scatter plot, alongside T-

score performance bands from the IMTP, addresses

some of the limitations of relying solely on the DSI

value. This approach offers a more comprehensive

evaluation of athletes' weaknesses and may guide

more effective strength training recommendations.

ACKNOWLEDGEMENTS

This research was funded by a project of University

of Latvia Nr. ZDA 2023/15 “An individualized

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approach to improving physical performance and

preventing injuries in basketball players, through the

development of athlete-specific sports profiles and

continuous monitoring of the daily training process”

and granted by the company “MikroTik” project

Nr.2314 “A set of devices for quantitative testing of

physical indicators of basketball players”,

administered by the University of Latvia Foundation.

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