If I could make a reasonable prediction of what each player will score for the upcoming season this will hopefully help me to select a better squad as outlined previously *********

At present, my algorithm uses the total points scored in the previous season to judge a players value. An example of this is the top 20 scoring players for the 2022 season.

Side Note: In this process I actually found some problems with my database which needed fixing *********

Using this to measure a players value led to picking the following squad.

You can obviously see alot of cross over between this squad and the top 20 scorers from 2022. It seems like I should be able to come up with a better metric for judging a players value.

I’m thinking that if I could get a rough prediction of the total score each player may score for the upcoming season based on previous years data, this could be a much better way to judge a player.

Training NN to predict total scores

TABLE (top 20 predicted scorers for 2023)

SQUAD (using NN pred as value)

This looks better on face value, but lets investigate by simulating the results from previous seasons to test

GRAPTH (results of simulations)

The total cost of the selected players is $3,504,300

I now have $7,945,700 left and 10 squad positions to fill with gun players. I want the players who will give us the highest scores week in week out.

To achieve this I have an algorithm that will maximise value of leftover players within the remaining budget. You can read more about that here ******. How do I judge a player’s value? I’ll get tot that later

Running this algorithm for the remaining 10 players with a budget of $7,945,700 we get

You will notice at a glance, that some positions will become full and I will not be able to add all these players to the squad. But I just go ahead anyway and start adding these players.

I was succesfully able to add the first 6 players, until I reached Alex johnson who could not be added as CTW was full and a positional swap wasn’t on the cards either. Read about positonal swaps here *******

So now my squad currently looks as follows

So I now need 3 front row forwards and 1 hooker. But I just rerun the algorithm for 4 players with a new budget of $3,226,000.

This will no doubt pick players that aren’t in these positions but I don’t want to remove these players altogether as a positonal swap may be available in some cases. If a player cannot be added, I then remove them from the player pool to avoid an infinite loop.

Running again, now with budget of $1,825,500 and players needed = 2

Now we need 1 player with a budget of $1,034,800

This give me my complete squad

So success! The algorithm has picked a squad.

Is it any good? I think most SC players would say… no, it’s terrible!

Although many of the gun players are seemingly top players. I can’t help but feel that there were better choices along the way. This is especially true as we ended up $330,300 under budget. Where is Tedesco, Cleary and the like?

The reason for this is likely because of how I have chosen to select a value for each player. At present, this is done by getting the total points scored for the last season.

This is clearly a pretty naive strategy. It would only take a player to have a rare spell on the sidelines for them to be severly undervalued. But in the other case you could have a player that will score highly in games but is injury prone and spends a lot of time on the sidelines. In which cases this should impact their value. I think I will next look into trying to train an neural net (AI) to try to predict how many points a player will score for the upcoming season. This can be used with the current selection algorithm to hopefully select better gun players.

Other things I noticed while running through this was that we ended up with 2 cheapie halfbacks because of a positional swap. This isn’t ideal, we definitely need to maintain at least one gun player in specialist positions (HOK, HFB, 5/8, FLB). Also I am probably selecting too many cheapies, this could be contributing to the squad coming well under the budget.