"Trend forecasting is much like archeology but to the future.” - Lidewij Edelkoort
Supporting a small group or entire team roster with player monitoring, is a growing demand with coaches and other support staff now. In order to reveal direction to what the data is saying, teams are looking to other fields and their use of statistics for an advantage. Unfortunately typical statistical analysis isn’t always sport appropriate, because combining human biology and sports training is unique. Several teams are investing into very expensive business intelligence tools and statistical analysis software, a good idea on paper, but in our experience an imperfect solution. When managing large amounts of player data, there is a temptation to aggregate or merge all the data to see relationships. The central repository approach is very popular because most teams realize the interaction of all variables is likely to be the cause of trends and patterns. The problem with merging too much data is that our eyes can get lost with information overload, so just the right amount of information is a better choice. Teams can still collect all the data they need, but management is not collecting as much as possible, but organizing and visualizing the right data.
In past blogs we have discussed how relationships exist with training loads, program design, and testing power. In this blog we will explore what common patterns exist with the CHECK™ system and how to interpret trends pragmatically. Our users have found that the device speed is indispensible and the accuracy of CHECK™ is more than enough to give confidence to decisions from the scores. Four key concepts are explained when working with CHECK™ data, and any sport can use our approach to help optimize training and competition.
In the previous blog with professional hockey, the pattern of neuromuscular fatigue was not obvious until the end of the season, with a drop in readiness occurring from a severe injury. One of the common requests we get is how to summarize an entire season of data with the CHECK™ system. At a glance the shape and slope of the chart can be seen more clearly by doing additional regression analysis after the season is complete. Responding too much to what is observed acutely or too early may be an overreaction to a normal process of fatigue and recovery. The most straightforward option when analyzing the entire season is to look at what specific milestones of the year are changing, such as an athlete taking time off after the season or starting up training. Transition to the competitive season is another milestone, and some athletes cope better by responding faster. Our algorithm has a built-in feature to guide and warn coaches of overreaching and overtraining, and adherence to the warnings will protect teams from unnecessary fatigue. Seeing the big picture is about stepping back and seeing what is the most striking and general pattern over all of the scores. Whatever is seen, be it small or dramatic is a trend that can illustrate what is happening over a competitive season.
In the skeleton training blog we showed the repeating pattern of fatigue and training, and a shape of the chart each week was very similar. While the training program worked, it wasn’t the case in years past because much of the design changes were driven by failures of earlier athletes. One unfortunate reality is that some experimentation with adjusting each week is needed in modern sport. Trial and error is not ideal, but fine-tuning a weekly set-up is not a perfect process and human biology is not as predictable as we want it to be. Looking at weekly patterns exposes important discernments to the training program. Does the athlete feel and test ready at the time needed? After a game or competition, the athlete is expected to feel tired and test fatigued, but if a program drives the athlete into the ground before competition or on game day, something needs to change. The NBA and NHL are playing multiple times a week, and Professional Baseball is spending more time competing than resting, creating a conundrum of when to rest and when to train. Strength coaches can decide on volume and frequency to see how each week is responding from daily evaluation, a needed status update when the margin of error is so narrow. Coaches are often overly conservative and want confidence to know they are not doing too much, and using CHECK™ ensures that training is the optimal configuration.
The chart on the top is an example of nearly a dozen athletes tested with the CHECK™ system and you can see a very clear story. As time progressed, more athletes adopted testing, from seeing the benefits with their teammates and electing to get involved. In addition to more athletes requesting testing, the objective feedback enabled more confident and stronger decisions while athletes became more compliant in lifestyle choices with recovery. To help illustrate the similar pattern of the athlete’s neuromuscular fatigue, we used a stacked line chart to make comparison easier between athlete to athlete. Athletes will vary in how they respond to training but the goal is not about seeing what is different, it’s seeing what can be similar by adjusting training. Team sport athletes must practice and compete together to improve tactical abilities and rehearse strategy, so like it or not everyone must be on the same “wave length” during the week. A game on Sunday or Saturday will need everyone having excellent readiness at the right time, and small adjustments during the week will be needed so everyone can be ready together. Practices, while not counting on the win/loss column, must be productive and that means participation is needed and readiness is high enough to improve or maintain skills. Individual adjustments can be made in areas that can give individual choices, such as weight training and complimentary conditioning. A starter playing nearly all the available minutes will have a dramatically different 48 hours after a match, but three days later everyone is likely to be doing similar activities because of rest. Non-starters will likely do more training near competition, but at the end of the week they still need to be ready just in case.
Statistical significance is technically a point of value that elicits a competitive advantage, but real world advantages need to be dramatic to see it objectively. A 20-kilogram improvement in a squat exercise may be statistically significant in a research study with youth athletes, but if the football player isn’t improving his speed or helping him reduce muscle pulls, the training effect is not is not swaying performance improvement and must be reconsidered. Even when performance testing improves, it may not impact the game results much and that’s why marginal gains is a popular topic of choice with coaches. It’s not one variable that makes the difference; it’s the sum of all the parts that tends to turn the tide in favor. No definitive definition for marginal gains exists, but a simple explanation is that the margin is enough to separate a winner and looser, be it in a play between to players or the results of an entire season. It is assumed that athletes and coaches are managing the basics, or the first 99% before trying to get the last 1% that decides victory. The small things add up to a slight advantage, and if that margin is enough, could be the decisive factor in championships. When using the CHECK™ system with every workout, more optimized training accumulates and final results can be observed at the end of the season.
The competitive margin is decreasing each year slightly as information is more available, and those that best use their resources and get compliance will get more from what they have. The four methods of analysis are just the tip of the iceberg on what is possible when one collects good data over the long run.