What You Really Need to Know as a Strength Conditioning Coach 1

 

Aim and hypothesis

The aim of my research is to investigate whether players’ self-reported wellness metrics such as Sleep quality, Mood, Muscle soreness, and Body fatigue have a correlation with individual AFL game day performance.

 

Game day performance will be measured by coaches and player self-rating of 1 – 5 1 being a poor gamed and 5 excellent and champion data.

 

Wellness is critical for an athlete’s recovery between games and physical preparedness for performance in the upcoming game. (5)

 

There is research (2) on the impacts of wellness leading up to the game on running performance however no wellness data collected from AFL players on gameday.

 

My hypothesis would be that certain metrics would have a stronger influence on certain players than others. There may be a strong correlation between certain metrics that influence other wellness scores for example mood and body fatigue maybe closely link to quality of sleep.

 

Players are more likely to buy into something that they believe will help their game day performance.

Using this data will be helpful for support staff such as medical and high performance to provide AFL athletes with relevant wellbeing metrics and game day performance.

Staff informing players of this research can encourage athletes to discover or continue effective practices like sleep hygiene and mindfulness to improve their quality of mood and sleep.

 

Study design and participants

This study will be a primary prospective cohort study with mixed methods of quantitative & qualitative data. Realistically the study would be conducted at one AFL club over two seasons and due to small cohort having a mixed quantitative and qualitative approach would be appropriate to assist in applying and understanding the findings.

 

The source of this study will be AFL players playing from the Melbourne Football Club. The whole team over 22 rounds and finals would be required in this study which is up to 40 – 60 AFL players depending on how many listed players play over 2 seasons.

 

Measures and variables

From a quantitative approach using a number scale of 1 – 5 asking each player to rate the respective wellness question.

And from a qualitative point of view asking the athletes pregame “how satisfied they are with their weekly preparation regarding sleep, stress and how their body feels”.

 

Data collection will be gathered pre-game where each player included in the study will answer the following questionnaire:

Key wellness metrics:

rate (1 – 5) 1 = poor 5 = great

Sleep quality: players are educated to factor in, how many times they woke up during the night, how long it took them to get to sleep and the duration.

Mood players are educated to factor in if they feel flat, irritable, or overwhelmed or relaxed and content

Muscle soreness players are educated to factor in how sore their muscles feel barely being able to walk being 1 and 5 feel normal

Body fatigue players are educated to factor in their motivation and energy levels. Are they feeling fresh or drained?

 

Rating performance will be a mix of subjective and objective data 3 separate rows to help with analysis:

Row 1 Player rating from 1 – 5,

Row 2 Coaches rating from 1 – 5

Row 3 Champion data total score from the game.

 

After the first 4 games z scores can be calculated for each wellness metric to help determine the effect of individual fluctuations within each wellness rating.

 

To calculate a z score you simply create the following excel function: weekly rating (4) – minus the four-week average (3) divided by a rolling 4-week deviation of recent 4-week (0.8) z = 4 – 3 /0.8 = 1.02 in percentage %102.

 

Performance will be determined by looking at each player’s total

AFL player rating score using the Champion data algorithm in addition The Melbourne football coaches will rate performance for each player from 1-5. (1 – 5 1 = poor 5 = great)

 

Data collection protocol

On player arrival for each home and away game players will fill out a questionnaire displayed below table A via a wellness app like edge 10. Athletes would also be followed up by staff post-game during if they rated below 2 to further investigate the context behind the poor rating. We can then export the data from edge 10 to an excel pivot table which can help us collate the data and make it easier to analyse the player’s wellness data when looking for correlations and trends in performance.

 

Table A

 

From there champion data player rating and coaches’ votes are collected and collated and z scores are calculated for analysis.

At the end of the 2 years of study closely going through the data and looking for trends in the data such as high z scores of sleep result in a high probability in in consistent performances on game day. Applying the findings to back up the hypothesis or challenge it. The key part of this research will also be making sense of the qualitative comments made by the players that rated below 3 for any wellness metric. This may come in handy for staff to help finding solutions for the players.

Data analysis

The issue this research is trying to solve is how much subjective markers influence game day performance and therefore what are the key ones to focus on from a development point of view.

How do we analysis the data? Interpreting the data to help determine answer questions such as:

What is the relationship between wellness metrics and high performance?

What might be the key causes from a preparation point of view for high performance in AFL football?

Does one factor have a significant affect or is a mix of all metrics that need to be taken into context.

Strength & conditioning coaches in the AFL recognize the importance of wellness as research shows most teams have some form of wellness data collection for load monitoring. Why not add it in as a performance measure as well?

Perhaps we find some info that challenges assumptions like body fatigue and muscle soreness increases match day performance.

Ethical considerations

Limitations of this study would be getting every team on board and even if we could get every team on board for the 2-year study gathering the data in a timely manner would be another issue, as some if not most clubs would want to keep the data to themselves.

From an ethical point of view the club may have a clause on when the data can be released as this is a prospective study over a few years hopefully this wouldn’t delay the publish doubt.

The high turnover rate of Australian Rules Football playing lists will be an issue as we won’t have the same playing list every week and the list will change slightly each year.

Another limitation and potentially why no team has researched game day data on record is due to the players not wanting to be interrupted from their game day routine, potentially some players may refuse to be involved in this study further reducing the cohort size.

Further exclusion considerations if someone is struggling with a mental health issue than the wellness data will likely be compromised and therefore the player would need to be e removed from the study and any player coming back from a long-term injury for example players that have been out of the game for a year will also have different experiences to the playing group as they adjust back to the game.

Anticipated outcomes

Anticipated outcomes I think individual variance will be high amongst this small cohort some may report poor wellness and perform highly others may report great wellness and perform well.

Other factors other than wellness will influence therefore these outliers will no doubt pop up through the study.

Hoping we can find some clear findings such as how important consistent rating scores are and therefore low z score fluctuations for the playing squad. Suggesting how important players’ routines are.

Looking at how factors such as away games, the shorter time between games, and wins or losses affect the data. I would suspect finals and or big games may have a gap between experienced players’ wellness reporting and new players.

References

  1. Gallo TF, Cormack SJ, Gabbett TJ, Lorenzen CH. Pre-training perceived wellness impacts training output in Australian football players. Journal of Sports Sciences [Internet]. 2016 Aug [cited 2022 Jun 3];34(15):1445–51. Available from: https://search.ebscohost.com/login.aspx?direct=true&db=s3h&AN=115584834&site=eds-live&scope=site
  2. Ryan S, Crowcroft S, Kempton T, Coutts AJ. Associations between refined athlete monitoring measures and individual match performance in professional Australian football. Science & Medicine in Football [Internet]. 2021 Aug [cited 2022 May 24];5(3):216–24. Available from: https://search.ebscohost.com/login.aspx?direct=true&db=s3h&AN=151190664&site=eds-live&scope=site
  3. Lathlean TJH, Gastin PB, Newstead SV, Finch CF. A Prospective Cohort Study of Load and Wellness (Sleep, Fatigue, Soreness, Stress, and Mood) in Elite Junior Australian Football Players. International Journal of Sports Physiology & Performance [Internet]. 2019 Jul [cited 2022 May 24];14(6):839–40. Available from: https://search.ebscohost.com/login.aspx?direct=true&db=edb&AN=137415757&site=eds-live&scope=site
  4. Gallo TF, Cormack SJ, Gabbett TJ, Lorenzen CH. Self-Reported Wellness Profiles of Professional Australian Football Players During the Competition Phase of the Season. Journal of strength and conditioning research [Internet]. 2017 Feb [cited 2022 May 24];31(2):495–502. Available from: https://search.ebscohost.com/login.aspx?direct=true&db=mdc&AN=27243912&site=eds-live&scope=site

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