Scholes to do a Solskjær?

After a mere 718 games playing for the world famous Manchester United, winning almost every honour possible, Paul Scholes (pictured) is finally living the dream – taking up the managerial reins at Oldham Athletic. It’s something Oldham fans have long hoped for, especially as during that time that Scholes took United to soaring heights, the Latics slipped further and further down the leagues.

There is still some way further down they could go, but 14th in League Two for the Premier League founder members, feels close to rock bottom.

However, Scholes could hardly have asked for a better first three fixtures. Naturally, at any level there are no easy games, but in the spectrum of winnable to nigh on impossible, Oldham have three of the more winnable games coming up – three home fixtures on the bounce against teams below them, between today and next Tuesday.

There’s a 55% chance they win tonight (1-0, 17%) against Yeovil, there’s a 49% chance they win on Saturday against Crewe (1-0, 10%), and a 59% chance they win against Morcambe next Tuesday (1-0, 13%). See the table…

Forecast Win (%)
League Two Score PR(%) P(H) P(A)
Tues 12 Oldham Yeovil 1-0 17% 55% 21%
Sat 16 Oldham Crewe 1-0 10% 49% 25%
Tues 19 Oldham Morecamb 1-0 13% 59% 18%

 

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Our Research in (Israeli) media: are transfers in the winter window really worthwhile?

The following article was initially published in Hebrew here by Ouriel Daskal for calcalist, an Israeli daily business newspaper and website: this is a rough translation.

Guy Elaad, a post-doctoral researcher in the Department of Economics at Reading University, has been conducting research into the winter transfer market in recent months. The study, in collaboration with Prof. James Reade and Dr. Carl Singleton at the University of Reading in England, examines the impact of the transfer window in January on the performance of football teams: “There are many studies on the impact of coach changes on performance,” This led Elaad, Reade and Singleton to examine the effect of the winter players transfer market on the teams’ performance. “In other words, we check whether the teams are actually ‘getting stronger’ when they bring players in during the transfer window in the winter,” writes Elaad. “According to our results, the answer is no, both in the English Premier League and in the Championship. In fact, we find a significant negative effect from the amount of players who were replaced in the team (mainly the number of players who joined but also those who left) on the team’s performance in the months (Premier League) after the transfer window. “Even an examination of the total minutes actually played by players arriving in the transfer window shows that as more minutes are played by these new players, their team’s results are less favourable after the transfer window than before. ”

The study is not over yet, but the interim conclusions are very interesting. In general, signing each player is a short-term harm to the team, since there is a period of acclimatisation. If the player is at a very high level, it can neutralise the negative effect of the acclimatisation period. However, it is even possible to see exactly how too much action in the player transfer market can hurt teams.

The researchers looked at tens of thousands of transfers between 2008/09 and 2017/18 and saw that for each player who signed for the team and for each player leaving the team, the chances of good results in the following months decreased by 1 percent, relative to matches before the transfer window, if their opponents did not carry out any transfer activity. That is, if 5 players join and 5 players leave, on average the team has approximately a 10% reduced chance of winning a game after a transfer window. However, the data does indicate that a player’s high-quality transition (quality is measured by his value on Transfermarkt) may actually increase the chances of a team winning more games. For example, if a player joins for £45m, his new team’s chance of winning the next game is up 4%. For example, the acquisition of Virgil van Dijk for £75m helped Liverpool in the second half of the season And his quality made the team better for next season.

In any case, “the fact that the more players which are brought in the more performance after is hurt is evident from the data” says Guy Elaad. “The question is whether the owners of the teams are aware of the impact, and if so, why do they still sign players?” So far we have thought, and we still need to examine it in real life, about three possible explanations: A. There is an opportunity to bring a player we want – In the winter, it will hurt us this season, but it could be worthwhile for the following seasons: B. To increase the variance – we know that in the replacement of many players – the average effect is negative, but the chances of very positive impacts are also high, so it could be worth taking the the risk; C: the performance of the team is not the only objective when deciding on transfer activity – the owners of the teams want to avoid the criticism of fans / The media / sponsors that they did nothing at the window, especially in case players left the team. ”

Other studies have shown that staff stability is a virtue of achievement. According to the CIES Football Observatory, “Stability in the roster gives teams an advantage over rivalry – at the sporting level (better results in the medium and long term) and at the economic level (financial savings and easier integration of young players).” “Stability” is measured by CIES according to the average stay time of each player on the roster and the percentage of new players on the roster. CIES show that over the years, the less staff changes, the more the faculty spends. Already in 2001, the University of Southampton demonstrated in its Premier League research that “stability leads to success.” The social psychologists of the university calculated the stability of each squad in relation to the team’s position in the table and found that teams with small turnover were more successful than teams with high turnover. “Football is a complicated teamwork,” said Dr. Mark van Vogt, director of research, “Football is not built on individual talent, but mostly on the players’ ability to work together with other players as a team that tries to beat the team in front of it.” According to van Vogt: Players who rely on each other and the abilities of their teammates are familiar to them, meaning they understand each other’s strengths and weaknesses together with strong faith and correct guidance will succeed – against all odds. “Van Vogt concludes:” Instead of investing in new players and changing the staff , Clubs need to develop team capabilities of their players and ensure that everyone knows each other. ”

In other words, signing players often does not fit into professional-rational-sports thinking, which strives to create more stability in the team and build it with players who are more suited to each other. If owners want to help the team in the middle of the season, they do not necessarily have to buy new players, but they have to invest a lot of money in purchasing a break-even player, and if not, then simply take the players out for vacation or mid-season. That way, with less money he can make more connections, better communication between the players and a better team. In short, the winter market players should buy a lot of money or not buy at all.

Lower Leagues, FA Cup rearrangements (Jan 29)

Matches rescheduled due to FA Cup involvement are taking place this evening in the lower leagues. In League One Luton will look to take a decisive step towards the title by pulling away from Portsmouth with a 1-0 win (17%).

Forecast Win (%)
League One Score PR(%) P(H) P(A)
Blackpool Wycombe 1-0 20% 48% 26%
Bradford Shrewsbury 1-1 12% 36% 37%
Bristol R Peterboro 1-0 10% 41% 32%
Gillingham Accrington 2-1 7% 43% 30%
Luton Portsmouth 1-0 17% 48% 26%
Oxford Barnsley 0-1 10% 27% 46%

In League One, three 1-0 wins for the home teams.

Forecast Win (%)
League Two Score PR(%) P(H) P(A)
Forest Green Mansfield 1-0 14% 41% 32%
MK Dons Oldham 1-0 15% 40% 33%
Newport Co Port Vale 1-0 18% 49% 25%

Lower League Supplement (22 Jan)

Four rearranged matches take place tonight in League One and Two. An important principle of forecasting is updating forecasts when new information becomes available. Since the initial week when these fixtures were supposed to have been played, more matches have taken place. Managerial changes may have taken place, playing personnel may have changed, and cup competitions may have been exited from.

Forecast Win (%)
Score PR(%) P(H) P(A)
AFC Wim Fleetwood 1-0 7% 40% 33%
Plymouth Walsall 1-0 9% 45% 28%
F Green Grimsby 1-0 20% 53% 22%
Yeovil Lincoln 0-1 13% 31% 41%

Lower Leagues, R24 (Boxing Day)

It’s that time of the year, one of the dates you always look to when they release the fixtures in the middle of June or July – who are we playing on Boxing Day? It’s a little deflating when you find it’s Carlisle United away (with no disrespect due to Carlisle, it’s just not very near).

Most likely, many supporters in League One felt as deflated as most Oldham fans (well, those who have no beef with John Sheridan or Anthony Gerrard), as there are few obvious Christmas Crackers. Although Barnsley vs Peterborough is a good old fashioned play-off six pointer, Posh fans will hardly be relishing that trip up the A1. And The Model thinks their fans should better nurse hangovers than make the trip, as a 1-0 defeat is most likely (11%).

Forecast Win (%)
League One Score PR(%) P(H) P(A)
Accrington Shrewsbury 1-1 11% 38% 33%
AFC W Plymouth 1-0 11% 57% 18%
Barnsley Peterboro 1-0 14% 45% 27%
Burton Wycombe 2-1 10% 40% 31%
Coventry Charlton 1-2 9% 30% 41%
Fleetwood Doncaster 1-1 11% 34% 37%
Gillingham Portsmouth 1-2 10% 28% 43%
Oxford Southend 1-0 12% 60% 17%
Rochdale Blackpool 0-1 12% 23% 51%
Scunthorpe Luton 1-2 7% 16% 61%
Sunderland Bradford 3-0 8% 46% 26%
Walsall Bristol R 1-0 9% 60% 17%

In League One, the Model thinks the aforementioned trip to Carlisle will be worthwhile for Oldham fans, as a 2-1 win is most likely (8%). The author would like to add he thinks this will not happen.

League Two is somewhat unusual in that the top two are not the teams that have impressed most relative to expectations. The top two expectations exceeders both have  further opportunities to exceed expectations, as The Model has them both to lose 1-0 on the road (Forest Green at Newport (7%), Bury at Mansfield (11%)).

Forecast Win (%)
League Two Score PR(%) P(H) P(A)
Cambridge Crawley 1-0 12% 43% 29%
Carlisle Oldham 1-2 8% 24% 48%
Cheltenham MK Dons 1-1 6% 33% 38%
Colchester Stevenage 1-0 14% 48% 24%
Crewe Lincoln 0-1 13% 15% 63%
Exeter Yeovil 1-0 14% 48% 25%
Mansfield Bury 1-0 11% 39% 32%
Newport Co F Green 1-0 7% 48% 25%
N’hampton Swindon 1-0 15% 47% 25%
Notts Co Macclesfield 1-1 12% 38% 33%
Port Vale Grimsby 0-1 10% 28% 44%
Tranmere Morecambe 2-0 13% 64% 14%

Final League Table Forecasts

We have updated the final league table forecasts (below) using all the information from matches before today (19th December).

These can be compared against the Model’s previous forecasts from before a ball was kicked this season, giving an idea of which teams are performing above/below expectations. [See also here for full versions of previous end of season forecasts made in August and October]

Most likely final position and chances of: winning the title, Champions League qualification, relegation, automatic promotion and making at least the playoffs:

English Premier League, 2018/19

Most likely pos.
Aug Oct Dec Title % CL % Rel %
1 1 1 Man City 83.8 100 0
2 2 2 Liverpool 16 99.8 0
3 4 3 Tottenham 0.2 89.8 0
6 3 4 Chelsea 0 67.2 0
6 6 5 Arsenal 0 22.4 0
2 5 6 Man Utd 0 20.6 0
15 7 7 B’mouth 0 0.1 0.2
7 10 7 Leicester 0 0 0.3
18 11 8 Brighton 0 0.1 0.4
16 15 8 West Ham 0 0 0.3
10 8 11 Wolves 0 0 2
12 12 11 Everton 0 0 1.3
17 9 12 Watford 0 0 0.5
14 15 13 C Palace 0 0 2.3
14 18 15 Newcastle 0 0 8.9
11 16 16 Burnley 0 0 13.8
16 17 17 S’ton 0 0 36.9
7 20 18 Cardiff 0 0 60.3
9 12 20 Fulham 0 0 84.1
20 19 20 Hudd 0 0 88.7

EFL Championship

Most likely pos.
Aug   Oct Dec Title % AP % Poffs % Rel %
5 14 1 Derby 20.4 36.1 75.3 0
11 3 1 Leeds 24.7 42.6 79.7 0
19 15 2 Norwich 14.9 31.7 71.9 0
22 6 3 Notts 8.6 16.2 51 0.2
3 2 3 W Brom 7.4 16.3 49.6 0
2 6 5 Swansea 4.6 10.8 38.3 0.8
1 12 6 Stoke 2.7 5.5 27 1
3 8 7 A Villa 3.1 7 33.2 0.2
24 16 7 Birm 3.4 8.3 33.2 0.9
7 1 8 Midd 2.9 7.5 36.1 0.2
9 5 8 Sheff Utd 1.5 3.2 19 2
5 10 10 Bristol C 2.9 5.4 23.3 1.2
12 17 11 Preston 0.8 2.9 17.3 1.6
16 6 13 Wigan 0.3 1.1 7.1 5.7
14 13 14 Blackb 1.1 3.8 20 1.3
22 20 14 QPR 0.7 1.2 11.2 4.3
18 9 20 Sheff Wed 0 0 1.7 21.7
15 18 21 Millwall 0 0.1 1.5 26.3
20 23 21 Reading 0 0 0.8 23.8
18 21 21 Roth 0 0.1 1.1 24.7
8 4 22 Brentford 0 0.1 1.1 23.6
15 22 22 Hull 0 0 0.4 31.2
23 19 23 Bolton 0 0.1 0.1 53.3
14 24 24 Ipswich 0 0 0.1 76

League 1

Most likely pos.
August December Team Title % AP % Poffs % Rel %
6 1 Portsmouth 30.9 52.7 88.8 0
1 1 Sunderland 30 50.1 88.4 0
24 2 Luton 15.8 32 77.6 0
3 3 Peterb 7 16.5 58.9 0.2
8 4 Blackpool 3 8.3 41 0.9
2 5 Barnsley 1 5 34.9 0.6
3 7 Charlton 4.1 11.2 42.4 0.5
10 7 Doncaster 3.3 9.2 45.6 0.6
24 8 Wycombe 0.9 3.4 22.9 2.6
19 9 Walsall 2.3 5.8 31.9 1.2
17 10 Fleet 0.3 1.4 15.6 3.6
24 11 Coventry 0.6 2.1 19.3 3.4
9 13 Burton 0.5 0.9 7.9 10.9
17 16 Oxford 0.1 0.2 3.1 17.8
22 16 Rochdale 0.1 0.5 5.4 13.6
21 17 Acc Stan 0 0.2 5.4 14.9
3 18 Scunth 0.1 0.1 1.5 26.6
11 18 Southend 0 0.2 2.9 18.5
9 20 Plymouth 0 0.1 1.5 24.7
1 20 Shrews 0 0.1 2.8 24.5
19 21 Gillingham 0 0 1.6 34
14 22 Bradford 0 0 0.6 43.6
18 24 AFC W 0 0 0 73.6
12 24 Bristol 0 0 0 83.7

League 2

Most likely pos.
August December Team Title % AP % Poffs % Rel %
3 1 MK Dons 56 84.7 96.6 0
15 2 FGR 6.1 29.4 62.8 0
6 2 Lincoln 15 44.7 78.6 0
10 3 Colch 2.4 17.6 48.2 0.1
5 3 Mansfield 4.8 23.3 55 0.2
9 4 Carlisle 2.1 13.7 40.2 0.6
21 4 Tranmere 4 18.5 49.5 0.2
4 6 Northam 1.8 8 30.3 0.8
9 7 Exeter 1.9 9.5 34.3 0.1
2 7 Oldham Athletic 1.5 11.7 38.3 0.4
1 10 Bury 1.6 11.5 37.9 0.5
14 12 Newport 0.8 8.3 27.7 0.7
12 13 Stevenage 1.2 7.4 29 0.6
10 15 Swindon 0.4 5.8 23.1 1.6
21 17 Crawley 0.1 0.9 7.6 5.8
24 18 Crewe 0.1 0.6 8.7 6.9
17 19 Port Vale 0.1 1.3 8.5 6.1
16 19 Yeovil 0 1.3 8.4 4.5
11 20 Chelt 0 0.4 4.7 12.6
24 20 Grimsby 0 0.7 5.9 7.1
19 23 Camb 0.1 0.4 1.9 22.6
22 23 Morec 0 0.1 1.3 27
5 23 Notts Cty 0 0.2 1.5 31.5
21 24 Maccles 0 0 0 70.1

Key (% likelihood of…):

  • Title: winning the league title
  • CL: qualifying for Champions League by finishing in the top 4.
  • Rel: relegation by finishing in bottom 3/4
  • AP: automatic promotion by finishing in positions 1-2/3
  • Poffs: at least making the playoffs by finishing in top 6/7

Which English Football League is the most “predictable” in 2018/19?

After watching their defender boot the ball out of the stadium, or the centre forward hit the corner flag with a shot from the edge of the box, lower league football fans can reassure themselves that the reason they turn up at dilapidated stadiums each week instead of the Emirates or Stamford Bridge really is because the Premier League is just too “predictable” (boring).

At least, that is what an unqualified comparison of the Model’s forecasts and actual outcomes so far this season suggests.

[There are many better ways to answer this question, e.g using a range of forecasts including bookmaker odds, or using the forecast density of all possible outcomes rather than just point forecasts. And not least because we have in fact tinkered with our Model design all season (probably making it worse) But you would probably just get a similar result anyhow… let us know if you don’t!]

The table below compares the Model’s forecast performance across all the English Football Leagues and Women’s Super League so far this season (up to date as of 12 noon, 2nd December).

The Model has forecast correctly 53% of results in the Premier League, the third highest of all six leagues looked at, only bettered by the relatively more “predictable” Women’s Super League and Women’s Championship. The worst forecasting performance by the Model has been in League One with just 40% of results correct, closely followed by the Championship with 41%.

In terms of exact scorelines, the Premier League is the most “predictable” in England, with 13% of match scores being forecast exactly right by the Model. Despite being the most predictable in terms of results, the Women’s Super League is least predictable in terms of exact scorelines, probably explained by the high variance of goals scored across matches.

In terms of average “Lawro” points per game, which in effect put weights on getting the correct result relative to also getting the correct scoreline in any match with a single forecast, the Premier League is comfortably the most “predictable”, with on average 9.5 points per game achieved by the Model so far this season (note, tipsters Mark Lawrenson and Paul Merson perform similarly highly on this metric at 8.1 and 9.6 points per game, respectively).

Scores (%) Results (%) “Lawro” p.p.g. # matches
Premier League 13% 53% 9.5 137
Championship 11% 41% 7.8 238
League One 5% 40% 5.6 237
League Two 12% 44% 8.1 237
Women’s SL 4% 73% 8.6 49
Women’s Champ 8% 56% 7.5 48