Hope I word this correctly.
As of late I have noticed that many people talk about Analytic or advanced stats but I think not all understand them. It is not as simple as 1 + 1 = 2 here. I have an advance degree from London School of Economic(Take five to chuckle and remember--this place is where I come to relax and turn off my brain) and analytics plays a large part of my degree as in business and how numbers are crunched across the board and seven ways from Sundays. (Right now I building a budget based upon my forecast based upon business analytics for how BREXIT will affect all hotels in my chain) For the numbers being tossed out I think people need to explain their interpretation of the numbers and the how and why they got their decision. To explain analytics more and more business are using SWOT or a hockey version of STEEPLE or PEST(these version factor in where they were developed, amount of games they played, international play, amount of games played per year, injured suffered, type of equipment used)
Just tossing out numbers and saying there is the proof--is not how analytics work. Explaining what the numbers mean is the proof and how you got the numbers to make your decision.
For those he keep using analytics do you have advance statistical training or an advanced degree in data collection and analysis method? Most don't(sorry) most have downloaded an excel spread sheet or created one based upon other peoples information and after typing in stats--they get numbers and think is the conclusion and is proof. That is just step 1 of larger process to understand analytics and their use. Data Collection is the easiest part--it is the method used to analyse the data gathered where many people fail to grasp the complex nature of the project.
Not picking on any in particular, I just see people posting small sample sizes and proof of analytics of a certain player or team.
Here are some(small example) of the data collection that is done for D-men
1) Time on ice
2) Partner
3) Shift length(per period and per game)
4) Special teams play(both PP and SH)
5) Play on back to back nights and game per 7 days
6) Quality of competition
7) Time between shifts
8) Time in offensive and defensive zones
9) Quality of line mates beyond that of D-Partner
10) Breakdown of each shift (Shifts are usually broken down into 10 second segments) Break down of period play--each period has 3 ten minute segments-first ten minutes of a period, last ten minutes and 5 minute to 15 minute)
11) Continues play(this is explained best when we have say 5 to 10 minutes without a whistle and a D man may have 3 to 7 shifts during that time)
Just having stats and not explaining the methodology put into is were many people who talk analytics fall down/
Will give three examples.
1) Kris Russel--Corsi sucked--but when you look what lines he played against and who his partner was, situations he played and line mates--it clears the picture a bit
2) Jeff Petry. Petry was and is a 3 to 6 d-man who the oilers kept playing as a top pairing D-man on most nights. His perfect Ice time should have been between 15m to 18 minutes per night, but on many nights he was playing 20+ minutes a night and on back to back to nights. More then that he got worn down. the oilers expected too much too soon from him and his numbers were bad and well it got ugly. He went to Montreal--they changed what TYPE of partner he had, gradually increased his ice time to and limit his shifts
3) Adam Larsson. Lets be honest. We lost a lot posters in the 2 hours after this trade. Posters saw his numbers and went ape ****. Most had never seen the guy play more then two games a year. I saw him play as a 17 year old in Skelleftea and he blew my mind. He is not fancy but he is good and his numbers do not show for the most part how good he has been.
Yeah, I know--will get the Did not read gif for this post. but it is an observation that not everyone who talks about analytics actually understands them, can not put them in context or explain methodology behind how the numbers were concluded