Is it possible at all to have a matrix showing each each player's score for each gameweek?

Thanks

Paul

Hi, Do you know why there seem to be three players data missing for this week in the scraped csv file? I've spotted Lewis Cook and a couple of others. Great resource, thanks for providing it for us all.

Paul

PeeDub

One small feature that would be useful is the ability to do a Rate My Team with no transfer restrictions (i.e. I'm using a wildcard.) The Dreamteam is close, but I don't quite have the funds to pull it off.

Tom S

Hi,

Thanks for providing this site.

Would it be possible to put up a list what you think are possible exceptions that could lead to inaccuracies in your predictions? Just generally I mean - not on a case-by-case basis.

I'm thinking things like: -

Injury recovery time

Player fitness

Travel fatigue

Weather

Inter-team social effects (e.g. a double act where the other player gets an injury)

Bad relations between players/coach/manager

Player issues outside of football

Changes in tactical approaches

Etc., etc.

mar

it would be useful if dreamteam for the current gw stays visibile until the end of the gw, at least..

mikecro

I have tried to model predicted points along the lines described in the academic paper and your intro. I struggle to interpret the algorithm which you then use to find optimal changes for the next n weeks.

Do you have any pointers to similar problems and solutions for a computer scientist (rather than a mathematician) to understand?

I have coded (in R) something which will pick a top 11 from my squad based on the forecast. And I can almost get my head around working through each member of the squad and then trying each potential replacement which doesn't break the constraints.

In my other scrabblings I found there is an algorithm for determining team strength and therefore fixture difficulty for partially complete leagues. The Perron-Frebonius method. Haven't got round to testing what it predicted earlier in the season with how the season actually turned out.

Michaelf

I have gone to Rate my Team, inserted my correct number, but it won't load?

KoolHerc

So, I was thinking about swapping Sigurdsson for Lallana and happened across this website - having considered doing my own statistical analysis and soon realising it's far too complicated! I have a couple of queries about the forecasts (and underlying data/calculations). Sorry if this is a bit long.

(1) In comparing the Sigurdsson / Lallana forecasts, I've noticed something that is either an error, or that I really don't understand. Let's use Lallana as a case in point.

His current total score (after GW 17) is 86 points. Giving him an average score (ignoring missed games etc) of 86/17 = 5.1 points/game. Yet his forecast is predicting around 1 point/game for the rest of the season.

At first I thought he was being penalised for missed games (e.g. dropped/injuries) so I went back through the historical csv files you have (great feature), which is where I found the quirk I don't understand. Specifically it's related to the data in the 8th column, which gives the average points/game.

If I look at the GW6 file (up to and including that week he started every game) his average points is "correct" at 40/6 = 6.7 - with his subsequent forecasts matching this type of average. In the next game he is only a sub and comes on for 20 something mins. Suddenly his average drops to 1.95 - and his forecasts drop accordingly.

I assume this must be some penalty due to him being a sub (a skim of the linked paper suggests that this is incorporated), but it seems a very heavy penalty. Furthermore, the numbers seem surprisingly neat to be such a penalty:

His average becomes - exactly - his "actual" average divided by 3. i.e. 41/7/3 = 1.952381 ...

Dividing his average by 3 seems a heavy penalty, and strangely neat. Moreover, it seems to stick as a factor of 3 permanently for all subsequent weeks - I would have thought, if this was the cause, the penalty would vary in magnitude dependent on how many games he doesn't start. For example, jumping forward to GW17 - his average is now listed as 1.686 ..., which is still - exactly - his actual average (86/17) divided by 3.

This leads to his forecast being (I think) unrealistically low as mentioned above - even taking into account he might not be starting every game.

This also seems to apply to all other players who haven't started all games. All their averages (and hence forecasts) are their "actual" averages divided by 3. Why 3??

Except Sigurdsson himself - who is listed with his "actual" average of 81/17 = 4.8 - despite the fact that he didn't start the very first game!

I really don't understand why there is such a neat (and constant) factor of 3 penalty to the average scores for players who aren't starting every game (except Sigurdsson). To me at least, this seems more probable to be an error?

Apologies if I'm just being stupid, but I really think it's highly unlikely Lallana will average only 1 point/game for the rest of the season!

(2) Sanchez's "actual" average is correct at 7.5 - IIRC he hasn't missed any games - but the current forecasts are more 9.something. Why the significant difference?

I could hand wave it as being dependent on the teams played compared to those to be played - but we're roughly half way through the season so they're about the same.

Maybe a home/away difference, but looking at the fixture list I'm not sure that tallies up. OK, Arsenal have played Man City and Man Utd away - but they've played Chelsea and Spurs at home - so there doesn't seem to be an obvious reason to suggest this is the cause.

SB

Small suggestion - the History tab has (presumably) auto-ordered the historical CSVs, but the way it does it means they go in a mildly unexpected order (All the 10s, before the single digits because 1 is before 2, despite 10 being after 2). Not sure the best way to sort it?

Fantasy Overlord

Yes, I was being lazy, fixed now! Cheers.

JohnStat

It's still early in the season so I propose that you get one team that the algorithm suggests (say, in the next game week), and then use that team throughout the season, only changing individual players as per game rules (one per game week). This individual player transfer would be taken from one of the algorithm's weekly predictions meaning that, if you already have him, you might not need to do any transfers that week. Secondly of you have him and his points prediction has dropped him from number one BUT still kept him in the top 5, there would be no reason to change him immediately. In essence what I'm proposing is to put the algorithm to a real world test and see how it competes

Fantasy Overlord

Hi John,

This is exactly the premise of the Beat The Overlord league. A team that is based off the optimal single transfer each week is the Fantasy Overlord team: https://fantasy.premierleague.com/a/team/244890.

The forecast distance is left at 5 weeks, and captaincy, vice captaincy, subs and keeper selection are all based off the Rate My Team suggestions. No wildcards are used, and no Bench Boost, Triple Captain etc.

Hi Overlord

Thanks for all the insights from this website.

Is it possible at all to have a matrix showing each each player's score for each gameweek?

Thanks

Hi, Do you know why there seem to be three players data missing for this week in the scraped csv file? I've spotted Lewis Cook and a couple of others. Great resource, thanks for providing it for us all.

Paul

One small feature that would be useful is the ability to do a Rate My Team with no transfer restrictions (i.e. I'm using a wildcard.) The Dreamteam is close, but I don't quite have the funds to pull it off.

Hi,

Thanks for providing this site.

Would it be possible to put up a list what you think are possible exceptions that could lead to inaccuracies in your predictions? Just generally I mean - not on a case-by-case basis.

I'm thinking things like: -

Injury recovery time

Player fitness

Travel fatigue

Weather

Inter-team social effects (e.g. a double act where the other player gets an injury)

Bad relations between players/coach/manager

Player issues outside of football

Changes in tactical approaches

Etc., etc.

it would be useful if dreamteam for the current gw stays visibile until the end of the gw, at least..

I have tried to model predicted points along the lines described in the academic paper and your intro. I struggle to interpret the algorithm which you then use to find optimal changes for the next n weeks.

Do you have any pointers to similar problems and solutions for a computer scientist (rather than a mathematician) to understand?

I have coded (in R) something which will pick a top 11 from my squad based on the forecast. And I can almost get my head around working through each member of the squad and then trying each potential replacement which doesn't break the constraints.

In my other scrabblings I found there is an algorithm for determining team strength and therefore fixture difficulty for partially complete leagues. The Perron-Frebonius method. Haven't got round to testing what it predicted earlier in the season with how the season actually turned out.

I have gone to Rate my Team, inserted my correct number, but it won't load?

So, I was thinking about swapping Sigurdsson for Lallana and happened across this website - having considered doing my own statistical analysis and soon realising it's far too complicated! I have a couple of queries about the forecasts (and underlying data/calculations). Sorry if this is a bit long.

(1) In comparing the Sigurdsson / Lallana forecasts, I've noticed something that is either an error, or that I really don't understand. Let's use Lallana as a case in point.

His current total score (after GW 17) is 86 points. Giving him an average score (ignoring missed games etc) of 86/17 = 5.1 points/game. Yet his forecast is predicting around 1 point/game for the rest of the season.

At first I thought he was being penalised for missed games (e.g. dropped/injuries) so I went back through the historical csv files you have (great feature), which is where I found the quirk I don't understand. Specifically it's related to the data in the 8th column, which gives the average points/game.

If I look at the GW6 file (up to and including that week he started every game) his average points is "correct" at 40/6 = 6.7 - with his subsequent forecasts matching this type of average. In the next game he is only a sub and comes on for 20 something mins. Suddenly his average drops to 1.95 - and his forecasts drop accordingly.

I assume this must be some penalty due to him being a sub (a skim of the linked paper suggests that this is incorporated), but it seems a very heavy penalty. Furthermore, the numbers seem surprisingly neat to be such a penalty:

His average becomes - exactly - his "actual" average divided by 3. i.e. 41/7/3 = 1.952381 ...

Dividing his average by 3 seems a heavy penalty, and strangely neat. Moreover, it seems to stick as a factor of 3 permanently for all subsequent weeks - I would have thought, if this was the cause, the penalty would vary in magnitude dependent on how many games he doesn't start. For example, jumping forward to GW17 - his average is now listed as 1.686 ..., which is still - exactly - his actual average (86/17) divided by 3.

This leads to his forecast being (I think) unrealistically low as mentioned above - even taking into account he might not be starting every game.

This also seems to apply to all other players who haven't started all games. All their averages (and hence forecasts) are their "actual" averages divided by 3. Why 3??

Except Sigurdsson himself - who is listed with his "actual" average of 81/17 = 4.8 - despite the fact that he didn't start the very first game!

I really don't understand why there is such a neat (and constant) factor of 3 penalty to the average scores for players who aren't starting every game (except Sigurdsson). To me at least, this seems more probable to be an error?

Apologies if I'm just being stupid, but I really think it's highly unlikely Lallana will average only 1 point/game for the rest of the season!

(2) Sanchez's "actual" average is correct at 7.5 - IIRC he hasn't missed any games - but the current forecasts are more 9.something. Why the significant difference?

I could hand wave it as being dependent on the teams played compared to those to be played - but we're roughly half way through the season so they're about the same.

Maybe a home/away difference, but looking at the fixture list I'm not sure that tallies up. OK, Arsenal have played Man City and Man Utd away - but they've played Chelsea and Spurs at home - so there doesn't seem to be an obvious reason to suggest this is the cause.

Small suggestion - the History tab has (presumably) auto-ordered the historical CSVs, but the way it does it means they go in a mildly unexpected order (All the 10s, before the single digits because 1 is before 2, despite 10 being after 2). Not sure the best way to sort it?

It's still early in the season so I propose that you get one team that the algorithm suggests (say, in the next game week), and then use that team throughout the season, only changing individual players as per game rules (one per game week). This individual player transfer would be taken from one of the algorithm's weekly predictions meaning that, if you already have him, you might not need to do any transfers that week. Secondly of you have him and his points prediction has dropped him from number one BUT still kept him in the top 5, there would be no reason to change him immediately. In essence what I'm proposing is to put the algorithm to a real world test and see how it competes