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MyFC Team Selector

MyFC Team Selector. Feasibility study Functional requirements Processes and Calculations Revision 3 Dave Twisleton-Ward (Davoloid) With special thanks to footy19 and Dr Lewis Griffin. What are we looking for?. What is the best formation for this game Opposition Upcoming games (easier/harder)

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MyFC Team Selector

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  1. MyFC Team Selector Feasibility studyFunctional requirementsProcesses and CalculationsRevision 3Dave Twisleton-Ward (Davoloid)With special thanks to footy19 and Dr Lewis Griffin

  2. What are we looking for? • What is the best formation for this game • Opposition • Upcoming games (easier/harder) • Who is best qualified to fill this position • Injuries • Form • Skills

  3. Wisdom of Crowds • Average choice should be correct more often than single expert • Decisions can be made more quickly conditional on • Diversity of members • Freedom of vote • Access to information • Working for the same common goal • No bias caused by influence of experts

  4. Final Goal • To produce a team lineup and formation that Liam and the Coaching staff can use • Requires: • Access to information about team • Problem of database & interface design • Not this document • A method for calculating the choices of many members

  5. My Previous examples • Complicated to explain • Complex calculation • Tied into increasingly complex interface • Brute force matching not even discussed past initial thought • Great flexibility and granularity (I think) • Could be relevant for future developments

  6. Realistically • Most people want: • Formation • Players Anything more complicated becomes difficult to synchronise with training regimes and implementation on matchday.

  7. Basic principle Chris McPhee Neil Barrett Mark Ricketts Liam Coleman George Purcell Luke Moore John Akinde Sam Mott Michael Bostwick Ronnie Bull Rob French Lance Cronin Peter Hawkins Sacha Opinel Gary MacDonald James Smith Paul McCarthy (captain) Danny Slatter Stacy Long Raphael Nade Chukki Eribenne Charley Hearn (on loan from Grays Athletic) Who are the best 11 players for a particular match?

  8. Aggregating selections • We each have a different opinion on who is best for each box. • Calculation of most common choice: Box 1…Box11 • Produces a player for each position • This is a simple calculation This is the starting 11, now where do they go?

  9. Formations We need to agree where each slot is for each formation. 3-5-2 4-4-2 4-5-1 1 1 1 3 7 3 7 2 3 4 2 4 2 4 5 6 7 8 9 5 6 8 9 5 6 11 8 9 10 11 10 11 10

  10. Selection ID Any selection can be described as: Formation choice + 11 Players in Slots = Selection ID (SID) Each player has a unique 2-digit number: PID Each formation has a unique 2-digit number: FID So we have a discrete 24 digit SID for each different formation: In this example: 020103021524251214170818

  11. Using SID E.g. 020103021524251214170818 • Gives us up to 99 different formations, and 99 players • 99 Formations IDs gives us the ability to distinguish between 4-4-2 defensive and 4-4-2 attacking, for example. • 99 Player ID’s allows us to keep historical records, probably up to 5 years? • We reserve 00 for when a formation is blank, or a player is not selected or becomes unavailable. • There may be algorithms that can “compress” this value to save storage / memory during processing.

  12. Benefits of SID • Simple method of storing and retrieving selections • Readable by humans • Selection process can now be done client side, sending only a text string to the team selection server. • SID is the same whatever interface is used • E.g. Flash, HTML drop down box • Submission via email or text message? • Increases ease of changing interface, • Allows analysis and export of data.

  13. Determining best SID • As members make their selections the SID is generated and added to a table. • If someone else makes the same SID (which we would expect) the count is increased. • We end up with a table of SIDs along with how many people have selected each one. • We should expect that many people will choose the same formation and lineup.

  14. Table of SIDs We might expect a normal distribution, with less deviation (blue line) as familiarity with players and access to information improves.

  15. A problem But all we’ve done now is picked the most common selection (modal average). Is this satisfactory? Maybe… • The variation between the top 3 may be so small that we can present 3 options to Liam What about the rest of the selections? We can take them into account with further calculations.

  16. The “real” best Formation Before we determine this we have to work out the difference between formations. The slots are in a physically different position, if only slightly. The tactical context of that position has also changed. We ask the question: How confident would we be that a player in 4-4-2, position 3 would be the right choice for 3-5-2, position 3? So let’s give it a confidence, c, where 0 would be no match, 1 would be perfect match. We would probably give slot 1 (the keeper) a confidence of 1 as this position never changes.

  17. Not convinced? Look how much players move when you change formation in the current team selector:

  18. Difference between Formations We know that there’s a good match, but what is required from a player in each slot is slightly different for each formation. 3-5-2 4-4-2 Slot 3 is reasonably close, so we’ll give it a confidence of 0.8 Slot 7 is not as close, so we have a lower confidence, say 0.5 The distance between two slots is 1-c We add up the distance for all 11 slots for this pair of FIDs. 1 1 2 3 4 3 7 2 4 5 6 7 8 9 5 6 8 9 10 11 10 11

  19. Map of formations We do this for each pair of formation IDs. This would be in consultation with Liam as it has to reflect the training regime and positioning. Once done, no need to go back. We now know the distance between each pair of FIDs, and can create a map. (see PublicWhip.org.uk) 4-5-1 5-4-1 4-3-3 4-4-2 3-5-2 If we need more tactical variation, e.g. 4-4-2 with offensive wingbacks? Simply create a new formation ID based on the previous 4-4-2. (this is why we need 99 formation ID’s) Only a slight adjustment to the distances for each pairing is needed, and can be verified with Liam.

  20. Mapping the players • We now work out the least distant FID taking into account how many people have voted for each. • (We may find that the most common FID was actually good enough.) • Now we’ve found the “best” formation ID, we can perform the basic “best 11” calculation we began with: • We map the players from the losing formation IDs using the confidence values we’ve established earlier. • We then total up the votes for each player in each slot as well as the mapped votes. • We don’t count players that have become unavailable, but we still keep the original SID data for reference

  21. Example mapping In the following example, 4-4-2 has been established as the best formation. We map the losing 3-5-2 selections to 4-4-2 so that we can count them and work out the starting 11. We do this for all SIDs with a losing FID. (e.g. 4-5-1, 4-3-3…)

  22. Example mapping What we’ve said here is that 200 people voted for this SID. Because we’re sure Cronin will be fine in goal in 4-4-2, our confidence was 1, so we count this as 200 votes for Cronin in slot 1. Barrett, on the other hand is in a very different position in slot 7 so we count only 140 votes for him in this slot under 4-4-2.

  23. End result • We might find that the second or third most popular SID might be the best. • But remember that there would probably be very little difference between the top 3 selections. • Now we have taken into account everybody’s opinion, which is more likely to be correct. (according to Wisdom of Crowds theory). • Because the calculations are so basic, this would not take long.

  24. Further uses • Because the selection ID is just a numerical string, these can be added to a members profile (for their own viewing or for audit purposes). • Or if this was kept in a database, member numbers just need to be added to each SID. • We might want to do this to examine trends: • Who is more likely to make the best choice, people who attend the matches or people who watch on TV? • Is this user consistently making “malicious” selections to sabotage the vote?

  25. Summary • Selection ID = Numerical string • SID = Formation ID+Slot1+Slot2…Slot11 • Physical location of each Formation ID determined with Liam • Confidence between slots in different formations also determined with Liam • Simple storage and calculation • Uses all contributed selections • Interface agnostic

  26. Development Process • Iterative – start basic, build tactical formations (e.g. 4-4-2 offensive) as confidence in system improves • Compare results after “tweaks” to confidence maps • Usability testing with mockups – member opinions • Need for good test data from previous matches!!!

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