Third Year (Part 2)

The last module of my degree was the volunteering module, like in my second year it was the best module. My work with the hockey team continued, however, my role changed. Last year I had taken the lead, but I took that as I had to do the brunt of the work. This year I was a senior analyst and I have developed as a manager, I realised it wasn’t my place to do it all, it was my place to steer the ship to ensure every thing is done. This year brought a new team to work with, and the aim to be more of a mentor than a manager. The year started of with recruitment, the second year SPA students where offered the opportunity, but as the inevitable is they where all drawn to the most popular sport football. There was obvious issues for us in the CPA as we have a number of sports to cover and it would not be beneficial to the teams learning if everyone worked with one sport. There was 4 other lead analysts all looking after a different sport and all having different approaches to how we ran are teams. I managed to recruit six analysts in the end.

The approach I took was one of mentorship, in the first weeks I used a guided discovery approach to get the guys in the swing of using the process I had designed for them to use. To be fair to the guys they quickly picked up on how it worked. My team of Ben, Tristan, Callum, Cameron, Joe and Mills formed a quick bond. I would like to say it was down to my approach but I just don’t know. In one of my first meeting with the guys I made it clear that the environment that I would like to create is one where we where all equal. However, I did also make it clear that if the time came where I needed to pull rank I would. This for the most part worked well, there was some hiccups.

At the start of the year we sat down, planned and shared out the work load. This for the most part worked really well, the load was shared evenly the guys began to not just use the system I had developed for them but began to learn how I up dated them, also how to develop there own workflows. There was an issue though as deadlines loomed the committed took on a larger work load and the surfers, surfed of the hard work of others. As a mentor I tried to look at why this was happening, I always think that if you stop doing something, theres a reason for it. It was at that point I sat down with each of the guys and tried to get to the bottom of what the problem was. After a long disscusions with each of the guys I felt that the way to combat it was ownership. We recorded what each person was responsible for and each had clear manageable goals, if they where completed praise was given, if not then they where the one to answer for it. There was also one thing that I made clear to them just because one person was responsible for a certain area does not mean that you could not help or support them with there tasks.

With the team working hard to provided the standard game coding on a game to game basis, it left me a little wiggle room to go out look into different things we code do to improve the service we provided. My first step was to look into GPS and the information it could provide. The CPA has 16 WIMU units, really useful as there is sixteen players in a hockey team. I used the units to collect the data of the players for a number of games, there was a learning step each time. To start of with I didn’t mark a point to which the game started, the issue with this being that the data I collected included the warm up and I had no near place I could clearly cut the data from. The next bridge I had to cross was getting the data from the devices in to Quiko, the program used to digest the data from the WIMU unit. One difficulty I found was the inability to transfer the data from the multiple units at once it was a laborious process  especially as I had to name each of the files as I went so not to loose track of who’s GPS was who’s.

One other consideration it took me a little bit to get my head round was what the Data meant in relation to the performance. An example of this is the total distance covered for by a player, my initial thought was the higher the distance the better the performance, a nieave thought now I know but it was my initial reaction. When I looked at the data in relation to the video it became clear all was not as it seems. The phrase working smarter not harder came to mind. There was one player imperticular that this became apparent with, he had the longest distance in the match with the highest number of sprints. Then when I reviewed the video I began to build a picture why it was so high. He was always out of position and was sprinting to get back to his true place. This is was where I felt the true difference between a data monkey and analyst.

One tool I used to find out if there was more irregularities was to compare the results of my collection to that of the collection of international teams data. I obviously did not expect there results to be identical, however, my aim was to spot an incremental similarity. It is difficult to explain, the long and the short of it is, if there results where miles different to that of there international counterpart there was something wrong.

Interestingly i still have more to write about so I’ll continue this next week.