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oaches and trainers who haven't the same exposure to data or similar logical thinking processes as them. Occasionally they have to communicate the data to playersalso. At the same time they have to explain it to coaches and front office whohave often developed predominantly through a coaching world. This backroom bandwidth challenge means that communication is the critical factor in how messages aredelivered to different people.

7 - Backroom Buddy- Teammates in the BackroomThe best way to both tackle the domain knowledge issue and the communication issue is to pair data analysts and performance experts. Pairing people allows a synergy to develop where the analysis of the data and results can be far more effective and faster. This also creates cross pollination of ideas and theories. Greater awareness of each others abilities and value is a side effect of the analysis progress made. Statistics must factor common sense. Statistically a Tomato isa Fruit. But you wouldn't put it in a fruit salad. Analytics rule is to inform decision making - not replace it. There must always be human, domain specific intervention at the end to review and interpret the data.

8 - Superman & Kryptonite- The 9 most heard words in sports are Because this is the way it's always been doneWith regard the value placed on analysis, it doesn't matter what a strength coachor athletic trainer think, few can interpret it without help. The president, hea

d coach and general manager of the organization are the most important people. But not all are accepting. Some old-school Head Coaches are to analytics what King Herod was to babysitting. Some accept it, many don't want it and those who don't get it won't admit it. Many staff who haven't come from that background or been exposed to it, have come up through scouting or coaching usually.

9 - Small is Big- Big Data doesn't work efficiently in team sport.We need to get over the idea that gathering data for a year or two and analyzingthen, is how to work. this approach can't, and won't work - simply because sport doesn't wait. You go to war with the Army you have. They

re not the Army you might want or wish to have at a later time. Donald Rumsfeld. Team sport analytics must beagile and reactionary to support decision making - not replace it. The process

must act fast on immediate data and act on the move. "A good plan, violently executed now, is better than a perfect plan next week." Gen. George Patton. It cannotwait for a perfect conclusion. This is faster and much more effective for everyone though, meaning that the actions are much closer the ground floor.

10 - Hard Target- The target is always movingPlanning to fire a rocket from a canoe is one thing, but aiming from and at a moving target complicates things even more. In Wayne Gretsky's immortal words - I skate to where the puck is going to be, not to where it has been. Any performance analysis approach operating now must aim with a 3-year window appreciating that the conclusions and data being collected now will be informing decisions in the future also.

11 - Analytics Continuum- Not all sport analytics are equalJust as analytics is a broad area. Performance analytics is a collection or morecorrectly a continuum. Think of it as a scale. On one side you have relativelystationary individual sports like archery and on the other side you have fluid,dynamic team sports like soccer. Along the continuum, moving from archery towards the centre, you have sports like sprinting, track cycling or olympic sports. Moving from the opposite side, from soccer where there are fewer breaks in play you have dynamic, stochastic team sports like basketball, rugby and football (NFL

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). In the middle you have mixed sports baseball. Performance analytics on the Individual side is relatively `easier' than team sports since the variable sources arefewer. To complicate things even more then, no team sport is exclusively just ateam - the role of certain individuals (i.e. Quarterback) need to be treated asindividuals some cases. And the opposite is also true. Even in Tour deFrance cycling or swimming you have team dynamics and team events. Finally, the diagrammatic complexity increases with the roster numbers per sport. Sprint teams can have 4 or 5 teammates, but an NFL roster might have over 90.

12 - Affirming, doubting, challenging & disrupting beliefs.Understanding the layers of analysis and suggested results allows us to peel away and interrogate the data. There are obvious, not so obvious and hidden suggestions in the data as we challenge the data we need to be open to challenging ourown preconceived beliefs.There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don

t know. But there are also unknown unknowns. There are things we don

t know we don

t know. Donald Rumsfeld.

2 Biggest threats to Analytics:

Analysts themselves - Overselling or creating false expectations for the industr

y sector.Relying on big data - Waiting for the perfect answer at the expense of agile methods.

Most exciting potential:

Measuring what doesn't happen - We currently only measure events. In team sport, identifying what doesn't happen is almost as vital. What was most likely to happen,but didn't has greater a potential to direct learning and performance sometimes than what does happen!