Football Musings

[wearthefoxhat]

I was just going off sofascore and then I found live streaming. Not being up to play with football like you guys I use Co pilot to explain formations and what the manager is wanting to acheive. When I got pictures in the 2nd game the formation was 4-2-3-1 at kick off and after the goal they went to 5-4-1 on defense which as co pilot said is they are parking the bus, with the home team having an Xg of 0.03 2nd half and .3 first there was little chance it was going to end up with 3 or more goals with 15 left.

Most football is play when I am stacking Z's and the A League has no Xg the MLS fits me perfectly 10.00am-6pm, so I can look for matches that fit this set up, I here Peter talking about a Racing setup so I see no reason why I cant have a football set up that suits me. Also I thought about the liquidity and as my bet are only $20 (10 pound) it shouldn't be a problem

The formation of a team is a moving feast. Certain managers favour a set formation irrespective of the opposition, others will do their homework and look for weaknesses, be it down the left/right flank, over the top or down the middle. A red card or an enforced change (injury) can mess up a previous game plan, but a simple booking can do so as well. ie: Defender gets a yellow card early on, and now has to tread carefully and not make a rash challenge in fear of a second yellow.

If you're at the testing stage, working within your comfort zone for staking and a % of the bank is more important. Ideally, compounding from the profits going forward is best. Even then, I'd keep within comfort zone thresholds as it might skew your thinking whether or not to stick or twist when in a trade.

I've expanded all my excel sheets across all the european top leagues and i've got it the stage of a simple copy/paste of key data and everything populates for a pre-off check. Web scraping and other VBA stuff is next on the list.

I didn't realise the importance of the formation, I was looking at Liverpool and their go to for EPL is 4-2-3-1 but in the CL or FA Cup it is 4-3-3 . So I ask Co pilot why and what are they trying to do, it makes for interesting reading.

I agree with all you said I did use bigger stakes (for me) than normal as I was going to trade out at Half Time or at some other point, but with the lack of goal creation chances I kept asking myself, Why? There was no reason to and it turned out that way.

I used AI to do a correlation of some of my stats to see what plays the biggest in goals so I just kept checking those as well as the game unfolded. But Co pilot did say "Correlation is not Causation" so I do keep that in mind.

What impressed me the most was I got the most I could from the game (profit wise) as I know there will be a game where two quick goals happen and in the blink of an eye it is 1-1, at that point anything could happen, but as I am making more from winning bets I will be more at ease to get out of those matches where it goes against me, old me would stay in and hope for the best.

All the best with the VBA stuff that sounds interesting

Back in the day, the England team tactics were based on the 4-4-2 formation. It was the bedrock and accepted as the way to play the beautiful game, until, it was recognised that other teams countered with more flair and panache adopting 4-3-3 and 4-2-3-1 and dominate the midfield.It then became which manager was the best tactically and could mix and match formations to counter the likely formations of their opposition.

Add the improved fitness of the modern player, counter and high press you suddenly have the Marcus Bielsa coaching and knowledge being used by the likes of Pep Guardiola later on down the line. With unlimited finance to spend on the best players, it's not a total surprise of how many games they won along with all the accolades.

The correlation is not causation was something I came up against when tackling the bivariate poisson model. The part where is correlates the home and away goals to add into the final calculation. Not being particularly maths minded, I just carried on assuming that it still has to be used within the formula, but not really knowing if there is a better formula to use. I accept that the model cannot react to outside influences not previously factored, so have to anticipate that the poisson prediction will make some form of adjustment.

The VBA stuff will be a learning curve that's for sure.

[andy28]

A very wise man once said "Compound interest is the eighth wonder of the world. He who understands it, earns it; he who doesn't, pays it." So will be looking to stake this way and compound winning, if I get any

[andy28]

Whats the chances Liverpool go with a full strength team this weekend, surely with another tough game Tuesday and with the points they have in the bank and Southampton's position on the table a full strength team seems unlikely.

[wearthefoxhat]

Whats the chances Liverpool go with a full strength team this weekend, surely with another tough game Tuesday and with the points they have in the bank and Southampton's position on the table a full strength team seems unlikely.

I thinks it'll be 4 games in 11 days.

The players all seem to want to play every game as they are fully buzzed up on all the success they've been having. Too risky to play the 2ndXI v Southampton as they can see what happened v Plymouth in the FA Cup. Be interesting to see how the odds move on the day.

[wearthefoxhat]

Here's some progress on my Poisson excel stuff. This one shows what it looks like after transferring a form table (using my own xG) into part of the sheet and then simply clicking the teams names, home and away, from the drop down menu.

It's telling me to proceed and must now input the match odds. (Betfair if market formed, Pinnacle if not)

It's already auto-populated the key data - created an projected xG, correct score odds and ranking, along with relevant market odds.

After Transfer.png

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This is what it looks like after the live market odds are input. It's now given me a trading risk sentiment, ranging from No Qualifier, Low-Medium-High. It's also assessing Edge %. Monchengladbach appear to be value at home by 11.54%, but the correct score ranking indicates 1-1.

After Input Odds.png

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I can now (if inclined) identify a whole weekends' match list in about an hour, in time order, ready to trade. Moving forward, these games, in theory, could be input into Guardian - Bet Angel, attach a .baf, and let it cook.

The principle, is to look for value/edge opportunities, that can set off a trade pre/IP, or, form part of a range of multi-acca's to essentially multiply the edge even more. (Chris Fawcett-esque)

[andy28]

Looking at my model I am seeing a pattern develop, it is tending to favour the underdog, especially short priced games where fav is <1.25. I guess this is because I am averaging so a team like Liverpool might get one that says their odds should be 1.4, throws it all out, I will have to keep an eye on which one is under performing.

[Atho55]

I`ve been betting on the HT market looking primarily when the score is 0-0 or 1-1 to construct a 5 match Acca. This was yesterday and close but no cigar. Had to put in returns as I recall (£520 thinking now) but at least it gives you an idea what can be achieved from a small stake and an example of what Fox is alluding to I believe.

Bet on HT Market.png

[wearthefoxhat]

Looking at my model I am seeing a pattern develop, it is tending to favour the underdog, especially short priced games where fav is <1.25. I guess this is because I am averaging so a team like Liverpool might get one that says their odds should be 1.4, throws it all out, I will have to keep an eye on which one is under performing.

One aspect I factored into my alerts, was to only focus on the value of the team that is favoured by the expected goals prediction.

So Liverpool were favoured 2.76 v 0.31. The xG Odds came out at 1.52 but the live odds were 1.12. The draw and away odds looks value, but could be considered inflated, as the xG in the model didn't favour them.

Liverpool v Soton.png

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The Portsmouth v Leeds game had a similar look to it.

Leeds price looked overbet (understandably), but the data suggested was a closer game. As the xG favoured Leeds, the Home/Draw could be considered inflated, as the Xg didn't favour them.

Ports v Leeds.png

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Another way I could look at it, maybe, is to lay2back trade (pre-off), these types of games, where the strong favourite doesn't look value, and are overbet. Then, using my in-play xG stats model, I could then see how the game is developing and check if my model is on the right lines.

[wearthefoxhat]

I`ve been betting on the HT market looking primarily when the score is 0-0 or 1-1 to construct a 5 match Acca. This was yesterday and close but no cigar. Had to put in returns as I recall (£520 thinking now) but at least it gives you an idea what can be achieved from a small stake and an example of what Fox is alluding to I believe.

Bet on HT Market.png

HT acca's! You could be on to something.

I was only looking at the multi-acca as a pre-off option. (Chris Fawcett-esque). So, if they were value odds pre-off, cover the 5 selections with 5xfourfolds 1xfivefold. That way, if you get one wrong, you'd still have a fourfold that would produce a profit.

[andy28]

Looking at my model I am seeing a pattern develop, it is tending to favour the underdog, especially short priced games where fav is <1.25. I guess this is because I am averaging so a team like Liverpool might get one that says their odds should be 1.4, throws it all out, I will have to keep an eye on which one is under performing.

One aspect I factored into my alerts, was to only focus on the value of the team that is favoured by the expected goals prediction.

So Liverpool were favoured 2.76 v 0.31. The xG Odds came out at 1.52 but the live odds were 1.12. The draw and away odds looks value, but could be considered inflated, as the xG in the model didn't favour them.


Liverpool v Soton.png

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The Portsmouth v Leeds game had a similar look to it.

Leeds price looked overbet (understandably), but the data suggested was a closer game. As the xG favoured Leeds, the Home/Draw could be considered inflated, as the Xg didn't favour them.


Ports v Leeds.png

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Another way I could look at it, maybe, is to lay2back trade (pre-off), these types of games, where the strong favourite doesn't look value, and are overbet. Then, using my in-play xG stats model, I could then see how the game is developing and check if my model is on the right lines.

Those examples you gave is what I see, I thought there must be something wrong with my model to be so far wrong in pricing up the market. There were 2 matches that stuck out for last weekends football, the Liverpool match 1 model said Southampton were not out of it, the other was Ipswich, that one had more than one model saying they were 20%-30% chance of winning. Both matches the underdog lost however they showed resistance which maybe why the model mispriced them.

Also as my model is a blend of my others I have added a Stdev into the odds they produce, I found the matches with the smallest Deviation tend to be correct, so those with big deviations maybe the matches to leave.

[wearthefoxhat]

I've included the In-Play model within the sheet. The salmon areas is the where the input is required.

The In-Play section changes when the remaining minutes is input.

Portsmouth appear value (they beat Leeds recently at home) This in turn creates value in other markets.

PortsvPlym New.png

[wearthefoxhat]

Here's the actual HT calculation. Seemed to work okay. (apart from the actual result so far)..

PortsvPlym HT.png

[wearthefoxhat]

Just following on:

Referring to last nights' game Nice v Auxerre at HT, the sheet accurately reflected the HT odds market based on Pre-off Poisson data.

I bolted on my interpretation of the Ben Michaels In-play xG spreadsheet, using my own xG calculations (not flashscore). He talks about how the xG can revert to the mean, and a trade can be made based on this assumption. Trading is opinion/maths based that needs to be backed up by data where possible.

The home team (Nice) overall picture and the in game stats, looked likely to score in the second half and so do Auxerre. (revert to mean)

Nice HT Set Up.png

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It was all going swimmingly, with Nice dominating attack/momentum and in-play stats, until a sending off for Nice 87th minute and a late equaliser in the 94th minute for Auxerre.

Att.Mom.png

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The Sofa-Score xG indicated Nice should have won the game, my own xG backed this up and wasn't too far off the original Poisson Pre-off prediction too.

xGResults.png

[TupleVision]

Referring to last nights' game Nice v Auxerre at HT, the sheet accurately reflected the HT odds market based on Pre-off Poisson data.

I bolted on my interpretation of the Ben Michaels In-play xG spreadsheet, using my own xG calculations (not flashscore). He talks about how the xG can revert to the mean, and a trade can be made based on this assumption. Trading is opinion/maths based that needs to be backed up by data where possible.

Interesting but I'm not sure how it can be mean reverting as xG only ever increases during a game? If a shot is missed it doesn't subtract from the previous xG?

Agree with your earlier assertion of xG though I incorporate it myself as a pre-match metric that I then compare to in-play, this plays a role in my model but certainly isn't weighted as the highest metric it's just a factor alongside many other inputs.

[wearthefoxhat]

Referring to last nights' game Nice v Auxerre at HT, the sheet accurately reflected the HT odds market based on Pre-off Poisson data.

I bolted on my interpretation of the Ben Michaels In-play xG spreadsheet, using my own xG calculations (not flashscore). He talks about how the xG can revert to the mean, and a trade can be made based on this assumption. Trading is opinion/maths based that needs to be backed up by data where possible.

Interesting but I'm not sure how it can be mean reverting as xG only ever increases during a game? If a shot is missed it doesn't subtract from the previous xG?

Agree with your earlier assertion of xG though I incorporate it myself as a pre-match metric that I then compare to in-play, this plays a role in my model but certainly isn't weighted as the highest metric it's just a factor alongside many other inputs.

Essentially, xG reversion means that teams performing significantly below their xG at halftime often improve, while those overperforming tend to slow down, provided, normal game conditions persist.

@Pre-off - Home xG 2.50 Away xG 1.50.
@HT Home xG 0.50 Away xG 1.00
The current score is 0-1.

Backing the home team to score in the second half could have value if odds reflect their poor first half rather than their overall ability.

In-play markets might overreact to the halftime xG, so if the home team’s price is inflated, it could present an opportunity.

If the match dynamics suggest the home team is improving (momentum, attacking pressure), a late goal for the home side is more likely than the odds might suggest.