1500x200_STATS_LP_BG
Premier League
PosTeamMPWDLGFGAGDPtsePtsChampsTop 4Relegation
1Leicester36221136434307779.3100.0%100.0%0.0%
2Tottenham36191346728397073.80.0%100.0%0.0%
3Arsenal36191075934256770.60.0%96.6%0.0%
4Manchester City36197106838306467.60.0%75.4%0.0%
5Manchester United3517994331126066.20.0%25.3%0.0%
6West Ham35151466043175962.70.0%2.7%0.0%
7Southampton36169115339145759.60.0%0.0%0.0%
8Liverpool351510105948115560.70.0%0.0%0.0%
9Chelsea35121211554874853.30.0%0.0%0.0%
10Stoke36139143852-144850.70.0%0.0%0.0%
11Everton35101411554964448.70.0%0.0%0.0%
12Watford35128153642-64447.20.0%0.0%0.0%
13Swansea361110153750-134344.90.0%0.0%0.0%
14West Brom361011153246-144143.10.0%0.0%0.0%
15Bournemouth36118174363-204142.90.0%0.0%0.0%
16Crystal Palace36109173646-103941.50.0%0.0%0.0%
17Newcastle United3689193964-253335.10.0%0.0%62.6%
18Sunderland35711174058-183235.00.0%0.0%57.3%
19Norwich3587203561-263134.10.0%0.0%80.0%
20Aston Villa3637262772-451617.90.0%0.0%100.0%

Last Updated: May 3rd

Serie A
PosTeamMPWDLGFGAGDPtsePtsChampsTop 3Relegation
1Juventus3628446918518894.0100.0%100.0%0.0%
2Napoli3623767431437680.80.0%100.0%0.0%
3Roma36211147740377476.50.0%100.0%0.0%
4Internazionale36197104734136469.60.0%0.0%0.0%
5Fiorentina36179105640166063.70.0%0.0%0.0%
6Sassuolo3614139453965554.60.0%0.0%0.0%
7AC Milan36141210474075455.00.0%0.0%0.0%
8Lazio3614913474705155.50.0%0.0%0.0%
9Chievo36131013434214949.90.0%0.0%0.0%
10Torino36129155051-14546.10.0%0.0%0.0%
11Genoa36127174146-54347.50.0%0.0%0.0%
12Empoli361110153746-94344.00.0%0.0%0.0%
13Atalanta361011153845-74143.80.0%0.0%0.0%
14Bologna36118173344-114142.80.0%0.0%0.0%
15Sampdoria361010164853-54042.90.0%0.0%0.0%
16Udinese36108183357-243841.50.0%0.0%3.0%
17Carpi FC36811173453-193536.70.0%0.0%37.4%
18Palermo3698193563-283538.10.0%0.0%60.0%
19Frosinone3687213571-363132.70.0%0.0%99.7%
20Verona36413193059-292524.90.0%0.0%100.0%

Last Updated: May 3rd

La Liga
PosTeamMPWDLGFGAGDPtsePtsChampsTop 4Relegation
1Barcelona36274510429758591.378.2%100.0%0.0%
2Atletico Madrid3627456016448589.914.9%100.0%0.0%
3Real Madrid36266410532738489.86.9%100.0%0.0%
4Villarreal36181084431136468.40.0%99.6%0.0%
5Athletic Bilbao36177125544115861.70.0%0.4%0.0%
6Celta Vigo36169115057-75757.70.0%0.0%0.0%
7Sevilla36141012494365258.20.0%0.0%0.0%
8Malaga36111213343314546.90.0%0.0%0.0%
9Valencia36111114444404443.50.0%0.0%0.0%
10Deportiva Las Palmas36127174449-54345.70.0%0.0%0.0%
11Real Sociedad36119164247-54244.40.0%0.0%0.0%
12Eibar36119164656-104246.00.0%0.0%0.0%
13Betis361011153150-194142.40.0%0.0%0.0%
14Espanyol36117183667-314041.50.0%0.0%0.0%
15Deportivo La Coruna36718114359-163937.20.0%0.0%0.0%
16Granada3699184265-233637.10.0%0.0%69.7%
17Rayo Vallecano36811174870-223538.00.0%0.0%26.7%
18Gijon3698193761-243537.70.0%0.0%51.2%
19Getafe3698193564-293536.60.0%0.0%52.5%
20Levante3678213466-322930.40.0%0.0%100.0%

Last Updated: May 3rd

Bundesliga
PosTeamMPWDLGFGAGDPtsePtsChampsTop 4Relegation
1Bayern Munich3226427515608287.899.9%100.0%0.0%
2Borussia Dortmund3224538031497782.10.1%100.0%0.0%
3Bayer Leverkusen3217695236165760.70.0%100.0%0.0%
4Borussia Monchengladbach32154136349144954.60.0%52.0%0.0%
5Hertha Berlin3214711414014951.20.0%27.9%0.0%
6Schalke 0432146124647-14851.00.0%15.5%0.0%
7Mainz3213712434124649.70.0%4.6%0.0%
8FC Koln321011113640-44143.20.0%0.0%0.0%
9Ingolstadt321010123037-74040.30.0%0.0%0.0%
10VfL Wolfsburg32109134348-53941.20.0%0.0%0.0%
11Hamburg SV32108143744-73840.40.0%0.0%0.0%
12FC Augsburg32910134048-83737.40.0%0.0%0.0%
13TSG Hoffenheim32910133849-113737.50.0%0.0%0.1%
14Darmstadt32811133650-143534.80.0%0.0%9.3%
15Werder Bremen3297164965-163435.90.0%0.0%15.6%
16Eintracht Frankfurt3289153351-183332.20.0%0.0%55.1%
17VfB Stuttgart3296174869-213337.90.0%0.0%52.2%
18Hannover 963264222959-302228.10.0%0.0%100.0%

Last Updated: May 3rd

Ligue 1
PosTeamMPWDLGFGAGDPtsePtsChampsTop 3Relegation
1Paris Saint-Germain3528529318758995.6100.0%100.0%0.0%
2Lyon36188106038226266.10.0%92.1%0.0%
3AS Monaco36161465444106263.30.0%80.0%0.0%
4St Etienne3617712423485862.30.0%14.5%0.0%
5Nice36169115339145762.70.0%12.7%0.0%
6Lille36141483827115658.70.0%0.6%0.0%
7Stade Rennes36131310515015252.70.0%0.0%0.0%
8Angers36131112383445050.50.0%0.0%0.0%
9Nantes361212123238-64835.50.0%0.0%0.0%
10Caen36146163651-154852.00.0%0.0%0.0%
11Montpellier3613716474524653.20.0%0.0%0.0%
12Bordeaux351113114655-94648.20.0%0.0%0.0%
13Marseille3691710464154441.80.0%0.0%0.0%
14Bastia36128163341-84449.40.0%0.0%0.0%
15Guingamp361110154553-84343.00.0%0.0%0.0%
16Lorient361013134655-94343.70.0%0.0%0.0%
17Gazelec Ajaccio36813153753-163736.60.0%0.0%56.5%
18Stade de Reims3699184055-153637.40.0%0.0%65.7%
19Toulouse36713164153-123450.20.0%0.0%77.8%
20Troyes3638252781-541718.00.0%0.0%100.0%

Last Updated: May 3rd

Champions League

Champions-League-Winning-Percentages

Atletico-vs-Bayern

Man-City-vs-Real-Madrid

League Table Stats

MP: Matches Played
W: Wins
D: Draws
L: Losses
GF: Goals For
GA: Goals Allowed
GD: Goal Differential
Pts: League Points

STATS Forecasts

ePts: Expected League Points
Champs: % chance of winning the league
Top 3/4: % chance of finishing in the Top 3 or Top 4
Relegation: % chance of being relegated

PROJECTIONS ANALYSIS ARTICLES

Watford's Odion Ighalo, left, celebrates scoring his side's first goal with Troy Deeney during the English FA Cup quarterfinal soccer match between Arsenal and Watford at the Emirates stadium in London, Sunday, March 13, 2016.  (AP Photo/Matt Dunham)
Real Madrid's coach Manuel Pellegrini from Chile, right talks to Cristiano Ronaldo from Portugal during their La Liga soccer match against Sporting de Gijon at the Santiago Bernabeu stadium in Madrid, Saturday, March 20, 2010.(AP Photo/Daniel Ochoa de Olza)
Sunderland's manager Sam Allardyce celebrates his victory over Newcastle United at the end of their English Premier League soccer match between Sunderland and Newcastle United at the Stadium of Light, Sunderland, England, Sunday, Oct. 25, 2015. (AP Photo/Scott Heppell)
Leicester manager Claudio Ranieri during the English Premier League soccer match between Leicester City and Southampton at the King Power Stadium in Leicester, England, Sunday, April 3, 2016. (AP Photo/Rui Vieira)

METHODOLOGY

STATS are projecting the chances of success for every club in the Champions League as well as each of Europe’s big five leagues: the Premier League, Ligue 1, Bundesliga, Serie A and La Liga.

We use our unparalleled data analytics technology combined with data collected by STATS to create our own European football projections, aimed at calculating the percentage probabilities for each team’s league finish in the big five European leagues. These will be based on proprietary STATS power ratings and algorithms analysing multiple data points associated with previous years of performance for each club. The League Projections represent the expected probability of each team winning the title, qualifying for the Champions League and ending the season in the relegation zone.

Once each team is given its distinctive STATS rating, we simulate each match of the season 100,000 times in order to provide a projection for the level of success expected for each club. The simulation eliminates ambiguity in the league table, and provides an accurate projection as a result of its large sample size.

The tables provide a purely objective projection for all European clubs, and projects their finish based on the results of our simulations. Each league table will be updated throughout the season to show how all of these probabilities change based on team’s current records, updated team ratings with factors including club form and strength of each side’s remaining schedule.

Frequently Asked Questions

How can I use this STATS feature?

STATS calculates the odds that each team, across the Top 5 European leagues, has of winning its league. Furthermore, we identify the odds of each team finishing in key spots in their respective leagues including the automatic Champions League Positions, the top half, bottom half and relegation places.

These odds are shown as percentages and can be sorted by any column:

CHAMPS – % chance each team has of winning the league
TOP 3 or 4 – % chance each team has of finishing in the key Champions League positions
RELEGATION – % chance of each team finishing in the relegation zone

Using our simulations, we have provided forecasts for the number of ‘Wins’, ‘Draws’ and ‘Losses’ for each team along with their expected ‘Goals Against’ and ‘Goals For’ for the season duration. These figures are taken into account when forecasting the final number of points expected for each team.

How many times do you simulate each match?

Each match of the season 10,000 times in order to provide an accurate projection for how each team will finish. The simulation eliminates ambiguity in the league table, and provides an accurate projection as a result of its large sample size.

How accurate are the STATS Projections?

The STATS Premier League projections were the most accurate of 60 predictions made by various fans, journalists, analytics experts and a few betting companies ahead of the 2014/15 season. More information can be found here.

Why do some teams have a 0.0% chance of winning the division, or finishing in certain positions?

This means that after simulating each match of the season 10,000 times, these teams had a less than 0.05% chance of ending in those positions. For the purpose of the table we have rounded percentages to one decimal point.

Do you take into account a team’s schedule (stronger or weaker teams)?

Yes. The simulation is done with each team’s schedule so if there are 2 matches left and Team A has the last 2 games at home against weaker teams and Team B has 2 away games against stronger teams this will be reflected in their rest of season projections and other related forecasts.

Many ‘relegated’ percentages don’t relate to the final positions. How is this explained?

This is why Monte Carlo simulations can produce the most accurate odds as opposed to linear formulas.

This is largely due to the point system in each league, where teams get 3 points for a win and 1 for a draw. Across the league, each team is given a ‘Goals Scored’ and ‘Goals Allowed’ rate. Due to the scoring nature of football, the higher scoring matches will translate into fewer draws than a team of equal strength that tends to play in lower scoring matches. In this way, some teams may encounter more draws during the course of the season when compared to another team, purely based on their goal scoring and goal conceding ratings.

This is then reversed in the bottom half of the table because higher scoring matches hurt below average teams. Since these teams are likely to score less than 50% of the total goals in their matches, more ‘draws’ are turned into ‘losses’ over the course of the season.

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