What MLB should learn from other Professional Sports
The biggest mistake all Sports Science programs make
Reverse Engineer Sports Science to win – Learn from CSI
Ironically, the most common error that most sports have made in sports science is the easiest to address and it’s something baseball can easily learn from.
When the sports science trend exploded there was a rush to invest in technology. The logic most adopted was forgivingly understandable: buy technology, invest in sports science, test and measure as much as possible. When errors or outliers were found – especially low numbers such as speed, strength – work harder to improve them.
“The forgivingly understandable error”
However, this was a completely erroneous approach based on one simple oversight – you can’t measure everything, let alone everything that truly matters. Simply increasing numbers doesn’t always make players better either – everything is related.
Teams soon realized a few fundamental challenges.
- They were measuring too much data and hadn’t planned for it
- Many teams didn’t know what data was actually important and what wasn’t
- They realized they were measuring largely physical metrics only
- Many teams had now more data collection to do but hadn’t man power to do it
- Worse still, many hadn’t the inhouse expertise to interpret the data
- Often because these developments evolved without coach involvement initially, getting their buy-in later was much more difficult and many coaches ignored sports science.
- Seeing the trend there was flood by technology companies to sell every kind of product to organizations that hadn’t the inhouse expertise to distinguish marketing from truth.
As you can see, this has led to confusion, vast sums misspent and varied results across the leagues.
I’ve seen this same mistake in every sport from NFL, NCAA, International rugby, Premier League soccer and the NBA. It all sounds fine in theory – but in the real world of the bullpen, where results matter, a pragmatic approach is needed to win.
The proper approach that teams are only starting to realize, is to work backwards. Yes, backwards. Rather than starting with the data, start the coaches experience first to identify the mistake or issue to be fixed. In other words, use what I refer to as “the CSI approach”. Start with the issue to be corrected, investigate and collect data on the specific issue and use specific technology and expertise to assess the evidence. This is an infinitely more effective approach than throwing technology at everything.
“Start with the evidence and work backwards”
Start with the manager and coaches. Let’s be honest, they know the sport best, they can identify the issues best and this is where sports science starts to work. The biggest benefit though is that starting with the coach means they are on board from the beginning. Start with the game, start with the coach and player and work backwards to solve the problem.
Be clinical too, don’t not start by testing everything. First of all it’s impossible to assess everything. Not everything that can be tested is important.
Secondly, every player is unique, especially at the elite level, so there is no such thing as normative or ‘average’ data.
Thirdly, using the ‘CSI’ approach always means that humans are directing technology, technology is not driving humans.
So what should your MLB organization do to ensure your sport science program succeeds?
- Do not measure everything – Measure only what is important
- Start with the game, the coaches to identify the issues to be fixed
- Ensure you have independent expertise inhouse or on call to make sure you’re not sold a myth
- Do not measure physical metrics only, psychological skill, and game intelligence metrics must be all combined into a holistic database
- Make sure you have the sports science expertise before the technology to operate and interpret the data
- There must be coach and manager buy-in for any chance of long term sustained success
- Identify a pathway, short- and long-term results, do not fire and forget.
- Always remember ‘The CSI Approach’ work with the evidence first, not the data.