Home/Sports/Predictions/Football/Bundesliga Germany

Bundesliga Germany Prediction Models

2026/2027

Flat Betting Model

Matches w/ Odds

$10

Stake Per Bet

+15.65%

Best: Away Underdog

-19.56%

Worst: Under 2.5

Odds Range Filter

1.0

10.0

Use minimum and maximum odds selection tool to identify the most profitable ranges for the Flat Betting football prediction model for Bundesliga Germany. After each change on the odds range, the system filters qualifying bets and recalculates profits and losses based on those selections.

Market ROI Summary

Flat $10 stake on every match from start to end of season. Profit/loss and ROI per market.

Market	Bets	Wins	Win %	Staked	Return	P&L	ROI	Best Str.	Worst Str.
Home Bet	63	32	50.79%	$630	$716.3	+$86.30	+13.7%	6	5
Draw Bet	63	13	20.63%	$630	$641.9	+$11.90	+1.89%	2	10
Away Bet	63	18	28.57%	$630	$565	−$65.00	-10.32%	4	11
Over 2.5	63	45	71.43%	$630	$715.2	+$85.20	+13.52%	8	3
Under 2.5	63	18	28.57%	$630	$506.8	−$123.20	-19.56%	3	8
BTTS Yes	63	36	57.14%	$630	$538	−$92.00	-14.6%	5	3
Home Favorite	42	25	59.52%	$420	$443.1	+$23.10	+5.5%	4	3
Away Underdog	20	4	20%	$200	$231.3	+$31.30	+15.65%	1	5
Favorite	63	35	55.56%	$630	$643.4	+$13.40	+2.13%	4	3
Underdog	63	15	23.81%	$630	$637.9	+$7.90	+1.25%	3	10

Staking Plan Comparison

Same bets, different stake sizing. Flat = $10 every bet. Kelly ¼ = stake based on rolling edge estimate (quarter-Kelly for safety). Confidence = stake proportional to implied probability (more on favorites, less on longshots).

Market	Bets	Flat P&L	Flat ROI	Kelly ¼ P&L	Kelly ¼ ROI	Conf P&L	Conf ROI
Home Bet	63	+$86.30	+13.7%	−$6.04	-1.46%	+$53.53	+8.57%
Draw Bet	63	+$11.90	+1.89%	+$3.10	+12.32%	−$46.82	-7.17%
Away Bet	63	−$65.00	-10.32%	+$3.16	+5.31%	−$130.22	-19.12%
Over 2.5	63	+$85.20	+13.52%	+$48.19	+7.01%	+$93.76	+13.48%
Under 2.5	63	−$123.20	-19.56%	−$5.63	-100%	−$133.62	-20.87%
BTTS Yes	63	−$92.00	-14.6%	−$15.37	-100%	−$88.87	-13.71%
Home Favorite	42	+$23.10	+5.5%	−$7.28	-4.95%	+$21.31	+5.11%
Away Underdog	20	+$31.30	+15.65%	−$5.99	-100%	+$48.13	+23.77%
Favorite	63	+$13.40	+2.13%	+$27.27	+18.73%	−$6.88	-1.09%
Underdog	63	+$7.90	+1.25%	−$30.97	-48.12%	+$50.30	+7.32%

Staking Methods

Flat Stake:: Fixed $10 on every qualifying bet regardless of odds or edge.
Kelly ¼:: Stake = bankroll × (edge / (odds−1)) / 4. Uses rolling win rate as probability estimate. Grows bankroll when winning, shrinks when losing.
Confidence-weighted:: Stake = (implied prob / rolling avg implied prob) × $10. Bets more on favorites, less on longshots. Uses your previous bets' average to calculate each stake — applicable in real time.

Flat $10 on this team's Win result in every match. Sorted by ROI.

Team	Bets	Wins	Win %	P&L	ROI
Hamburger SV	7	3	42.86%	+$67.00	+95.71%
Elversberg	7	3	42.86%	+$53.50	+76.43%
Werder Bremen	7	3	42.86%	+$40.90	+58.43%
Borussia Dortmund	7	5	71.43%	+$23.70	+33.86%
Freiburg	7	4	57.14%	+$6.40	+9.14%
Borussia M'gladbach	7	1	14.29%	+$5.00	+7.14%
Bayer Leverkusen	7	4	57.14%	+$4.20	+6%
Hoffenheim	7	3	42.86%	+$3.50	+5%
Mainz 05	7	3	42.86%	+$1.10	+1.57%
Paderborn	7	2	28.57%	−$1.80	-2.57%
Bayern München	7	5	71.43%	−$7.70	-11%
RB Leipzig	7	4	57.14%	−$8.90	-12.71%
Augsburg	7	2	28.57%	−$11.00	-15.71%
Eintracht Frankfurt	7	2	28.57%	−$14.70	-21%
Köln	7	2	28.57%	−$17.70	-25.29%
Stuttgart	7	2	28.57%	−$38.10	-54.43%
Schalke 04	7	1	14.29%	−$38.20	-54.57%
Union Berlin	7	1	14.29%	−$45.90	-65.57%

PPG Model

Teams

1.4

League Avg PPG

1.08

Home Factor

λ=0.85

WPPG Decay

Odds Range Filter

1.0

10.0

Use minimum and maximum odds selection tool to identify the most profitable ranges for the PPG football prediction model for Bundesliga Germany. After each change on the odds range, the system filters qualifying bets and recalculates profits and losses based on those selections.

PPG / WPPG Betting ROI

Backtested ROI betting $10 using incremental PPG — seeded with previous season data, blending into current-season results.

Strategy	Bets	Wins	Win %	P&L	ROI
PPG Favorite (1×2)	59	25	42.37%	−$84.10	-14.25%
PPG Home Pick	25	14	56%	−$34.70	-13.88%
PPG Away Pick	34	11	32.35%	−$49.40	-14.53%
WPPG Favorite (1×2)	63	28	44.44%	+$6.70	+1.06%
PPG Value (fair < market)	63	23	36.51%	+$44.30	+7.03%

Strategy Guide

PPG Favorite:: Backs the team with the higher PPG to win, whether home or away.
PPG Home Pick:: Bets on the home team only when PPG rates them higher than the visitor.
PPG Away Pick:: Bets on the away team only when PPG rates them higher than the home side.
WPPG Favorite:: Same as PPG Favorite but uses Weighted PPG (recent form weighted more heavily).
PPG Value:: Only bets when the PPG fair odds are lower than the bookmaker's odds — positive expected value. E.g. fair odds 1.80 vs bookmaker 2.10 = value bet.

Staking Plan Comparison

Same PPG bets, different stake sizing. Flat = $10 every bet. Kelly ¼ = stake based on rolling edge estimate. Confidence = stake proportional to implied probability.

Strategy	Bets	Flat P&L	Flat ROI	Kelly ¼ P&L	Kelly ¼ ROI	Conf P&L	Conf ROI
PPG Favorite (1×2)	59	−$84.10	-14.25%	−$41.45	-22.03%	−$73.12	-12.8%
PPG Home Pick	25	−$34.70	-13.88%	−$49.36	-56.03%	−$12.26	-5.12%
PPG Away Pick	34	−$49.40	-14.53%	−$11.59	-26.51%	−$69.89	-20.77%
WPPG Favorite (1×2)	63	+$6.70	+1.06%	−$1.24	-0.56%	−$34.84	-5.77%
PPG Value (fair < market)	63	+$44.30	+7.03%	−$24.57	-12.03%	+$80.71	+11.48%

Staking Methods

Flat Stake:: Fixed $10 on every qualifying bet regardless of odds or edge.
Kelly ¼:: Stake = bankroll × (edge / (odds−1)) / 4. Uses rolling win rate as probability estimate.
Confidence-weighted:: Stake = (implied prob / rolling avg implied prob) × $10. Bets more on favorites, less on longshots. Uses previous bets' average — applicable in real time.

Recent Results — PPG Predictions vs Actual

How PPG predictions compared to actual match results

Date	Match	PPG Pick	Prob	Fair Odds	Result	✓
25 Oct	Freiburg vs Bayern München	Freiburg	41.68%	2.4	1 – 3	✗
25 Oct	Hoffenheim vs Bayer Leverkusen	Bayer Leverkusen	57.33%	1.74	3 – 0	✗
24 Oct	Borussia Dortmund vs Eintracht Frankfurt	Borussia Dortmund	59.63%	1.68	1 – 0	✓
24 Oct	Augsburg vs Union Berlin	Augsburg	56.97%	1.76	1 – 1	✗
24 Oct	Köln vs Schalke 04	Köln	53.09%	1.88	0 – 0	✗

Upcoming Fixtures — PPG Predictions

Win probabilities for upcoming matches based on current PPG ratings

Date	Match	PPG Pick	Prob	Fair Odds
30 Oct	Elversberg vs Mainz 05	Elversberg	42.59%	2.35
31 Oct	Schalke 04 vs RB Leipzig	***	0%	0
31 Oct	Borussia M'gladbach vs Paderborn	🔒 Login or register for free to see predictions data.
31 Oct	Werder Bremen vs Hoffenheim
31 Oct	Bayer Leverkusen vs Stuttgart

PPG Ratings — All Teams

Points Per Game overall, home, away, and weighted (WPPG with λ=0.85 decay). Click column headers to sort.

Team	PPG ↓	H-PPG	A-PPG	WPPG	vs Avg
1Bayern München	2.43	3	2	2.49	+1.03
2Borussia Dortmund	2.43	2.5	2.33	2.31	+1.03
3Bayer Leverkusen	1.86	3	1	1.86	+0.46
4Freiburg	1.86	2.25	1.33	1.5	+0.46
5RB Leipzig	1.71	3	0	1.74	+0.31
6Werder Bremen	1.71	2.33	1.25	1.83	+0.31
7Elversberg	1.43	2	1	1.24	+0.03
8Mainz 05	1.43	0.25	3	1.35	+0.03
9Hoffenheim	1.29	1.5	1	1.57	-0.11
10Hamburger SV	1.29	2	0.75	1.45	-0.11
11Augsburg	1.29	1.5	1	1.04	-0.11
12Eintracht Frankfurt	1.14	1.33	1	1.13	-0.26
13Paderborn	1.14	1.5	0.67	1.34	-0.26
14Köln	1.14	2	0	1.06	-0.26
15Stuttgart	0.86	1	0.75	0.77	-0.54
16Union Berlin	0.86	1.25	0.33	0.97	-0.54
17Schalke 04	0.86	0.67	1	0.8	-0.54
18Borussia M'gladbach	0.43	0	0.75	0.66	-0.97

PPG vs WPPG — Biggest Divergence

Teams where PPG and Weighted PPG disagree most — indicates recent form shift

Freiburg

PPG: 1.86WPPG: 1.5Gap: ↓ 0.36

Recent form declining — WPPG dropping below season PPG

Hoffenheim

PPG: 1.29WPPG: 1.57Gap: ↑ 0.28

Recent form better than season average — WPPG rising

Augsburg

PPG: 1.29WPPG: 1.04Gap: ↓ 0.25

Recent form declining — WPPG dropping below season PPG

Borussia M'gladbach

PPG: 0.43WPPG: 0.66Gap: ↑ 0.23

Recent form better than season average — WPPG rising

Paderborn

PPG: 1.14WPPG: 1.34Gap: ↑ 0.20

Recent form better than season average — WPPG rising

Worked Example — Freiburg vs Bayern München

P(Freiburg) = 2.17 / (2.17+2.33) × 1.08

= 0.482 × 1.08 = 41.68% → Fair odds 2.4

P(Draw) = 0.268 × (1 − |diff|)

= 24.96% → Fair odds 4.01

P(Bayern München) = 1 − 41.68% − 24.96%

= 33.36% → Fair odds 3

Poisson / Correct Score

Select Match

1.68

Home xG (λ)

Away xG (λ)

4.68

Total E[goals]

2.19

League Avg Home

1.37

League Avg Away

Dixon-Coles Score Matrix — Elversberg vs Mainz 05

29 Oct · Each cell shows the probability of that exact scoreline. Dixon-Coles corrected (ρ = -0.13). · Upcoming

H \ A	0	1	2	3	4	5	6
0	1.6%	2.3%	4.3%	4.3%	3.3%	1.9%	1.0%
1	1.0%	5.5%	7.3%	7.3%	5.5%	3.3%	1.6%
2	1.4%	4.1%	6.1%	6.1%	4.6%	2.8%	1.4%
3	0.8%	2.3%	3.4%	3.4%	2.6%	1.5%	0.8%
4	0.3%	1.0%	1.4%	1.4%	1.1%	0.7%	0.3%
5	0.1%	0.3%	0.5%	0.5%	0.4%	0.2%	0.1%
6	0.0%	0.1%	0.1%	0.1%	0.1%	0.1%	0.0%

Most likely High Medium Low

Derived Markets

Home Win

19.33%

Fair odds: 5.17

Draw

17.92%

Fair odds: 5.58

Away Win

62.74%

Fair odds: 1.59

Over 0.598.41%/ Under 1.59%Fair: 1.02

Over 1.595.17%/ Under 4.83%Fair: 1.05

Over 2.584%/ Under 16%Fair: 1.19

Over 3.567.57%/ Under 32.43%Fair: 1.48

Over 4.548.35%/ Under 51.65%Fair: 2.07

BTTS Yes77.77%Fair: 1.29

BTTS No22.23%Fair: 4.5

Correct Score — Top 12

11-2

7.27%A

21-3

7.27%A

32-2

6.11%D

42-3

6.11%A

51-1

5.48%D

61-4

5.45%A

72-4

4.58%A

80-2

4.33%A

90-3

4.33%A

102-1

4.07%H

113-2

3.42%H

123-3

3.42%D

Dixon-Coles vs Plain Poisson

Where the low-score correction adds value

Plain Poisson

P(0-0) = 0.9%

P(1-0) = 1.6%

P(0-1) = 2.8%

P(1-1) = 4.7%

✗ Underestimates 0-0, 1-0, 0-1

Dixon-Coles (ρ correction)

P(0-0) = 1.6% (+0.7%)

P(1-0) = 1.0% (-0.6%)

P(0-1) = 2.3% (-0.5%)

P(1-1) = 5.5% (+0.8%)

✓ Fixes low-score underestimation

💡 Why does this matter? Draws in football are dominated by 0-0 and 1-1. Plain Poisson underprices these by 15–20%. Dixon-Coles corrects this, generating better ROI on draw markets.

Dixon-Coles Betting ROI

Backtested ROI using incremental Poisson/Dixon-Coles model — xG computed only from matches played before each bet.

Strategy	Bets	Wins	Win %	P&L	ROI
DC Value (1X2 edge > 3%)	51	27	52.94%	+$154.90	+30.37%
DC Home Pick (>50%)	30	18	60%	+$7.40	+2.47%
DC Away Pick (>50%)	7	3	42.86%	−$12.70	-18.14%
DC Draw (>28%)	5	2	40%	+$35.60	+71.2%
DC Over 2.5 Value	29	20	68.97%	+$42.90	+14.79%
DC BTTS Value	14	9	64.29%	−$7.30	-5.21%

Staking Plan Comparison

Same Dixon-Coles bets, different stake sizing. Flat = $10 every bet. Kelly ¼ and Confidence-weighted alternatives.

Strategy	Bets	Flat P&L	Flat ROI	Kelly ¼ P&L	Kelly ¼ ROI	Conf P&L	Conf ROI
DC Value (1X2 edge > 3%)	51	+$154.90	+30.37%	−$17.54	-5.46%	+$130.01	+25.4%
DC Home Pick (>50%)	30	+$7.40	+2.47%	−$59.98	-92.06%	+$13.13	+4.04%
DC Away Pick (>50%)	7	−$12.70	-18.14%	$0.00	0%	−$24.38	-38.84%
DC Draw (>28%)	5	+$35.60	+71.2%	$0.00	0%	+$41.57	+78.46%
DC Over 2.5 Value	29	+$42.90	+14.79%	+$15.34	+6.79%	+$47.26	+14.92%
DC BTTS Value	14	−$7.30	-5.21%	−$10.80	-14.24%	−$5.89	-4.09%

Staking Methods

Flat Stake:: Fixed $10 on every qualifying bet regardless of odds or edge.
Kelly ¼:: Stake = bankroll × (edge / (odds−1)) / 4. Uses rolling win rate as probability estimate. Grows bankroll when winning, shrinks when losing.
Confidence-weighted:: Stake = (implied prob / rolling avg implied prob) × $10. Bets more on favorites, less on longshots. Uses your previous bets' average to calculate each stake — applicable in real time.

Team Attack & Defense Strength

Attack/defense strength relative to league average (1.00 = average). Used to derive expected goals (xG) for each match.

Team	Att (H)	Def (H)	Att (A)	Def (A)	H GP	A GP
Bayern München	2.5	0.63	1.58	0.49	3	4
RB Leipzig	1.81	0.27	0.77	1.29	4	3
Augsburg	0.88	0.99	1.5	1.16	4	3
Bayer Leverkusen	1.24	0.51	1.38	1.15	3	4
Mainz 05	0.69	1.55	1.68	0.7	4	3
Freiburg	1.28	0.9	0.85	0.89	4	3
Werder Bremen	0.93	1.24	1.1	1.01	3	4
Hoffenheim	1.07	1.14	1.22	1.05	4	3
Elversberg	1.11	1.35	0.82	0.94	3	4
Borussia Dortmund	1.05	0.46	1.1	0.45	4	3
Borussia M'gladbach	0.94	1.7	0.73	1.23	3	4
Stuttgart	0.96	0.94	1.04	0.97	3	4
Eintracht Frankfurt	0.78	1.28	1.16	1.26	3	4
Köln	0.79	0.89	0.63	1.09	4	3
Union Berlin	0.67	1.08	0.49	1.45	4	3
Paderborn	0.49	0.72	0.25	0.38	4	3
Hamburger SV	0.65	1.18	0.57	1.11	3	4
Schalke 04	0.5	0.62	0.82	0.79	3	4

How Poisson Predictions Work

Step 1 — Expected Goals:: Home xG = Home Attack Strength × Away Defense Weakness × League Avg Home Goals. Each team's attack and defense ratings are calculated relative to the league average.
Step 2 — Poisson Formula:: P(k goals) = e⁻λ × λᵏ / k! — models the probability of scoring exactly k goals given the expected goals (λ).
Step 3 — Score Matrix:: Multiply home and away goal probabilities for every combination (0-0 through 6-6) to build a full probability grid for every possible scoreline.
Step 4 — Dixon-Coles:: Apply ρ correction to fix Poisson's underestimation of low-scoring draws (0-0, 1-1, 1-0, 0-1). This is the key improvement over basic Poisson.
Step 5 — Derive Markets:: Sum probabilities from the matrix to derive 1X2, Over/Under, BTTS, and correct score probabilities — then convert to fair odds for value identification.

Elo Rating

Current Elo Ratings

All teams ranked by Elo. Start rating: 1500. K-factor: 32. Home advantage: +65 Elo.

1Bayern München

1576

2Borussia Dortmund

1564

3Werder Bremen

1536

4Bayer Leverkusen

1534

5Freiburg

1522

6RB Leipzig

1513

7Elversberg

1498

8Mainz 05

1494

9Augsburg

1494

10Eintracht Frankfurt

1492

11Hamburger SV

1489

12Paderborn

1487

13Hoffenheim

1486

14Köln

1476

15Union Berlin

1475

16Schalke 04

1475

17Stuttgart

1455

18Borussia M'gladbach

1434

Elo → Win Probability Table

How Elo difference translates to win probability.

Elo Diff	P(Higher wins)	Fair Odds	Typical Matchup
0	50.0%	2.00	Evenly matched
50	57.1%	1.75	Mid vs mid-lower
100	64.0%	1.56	Top-8 vs bottom-8
150	70.3%	1.42	Top-4 vs relegation
200	76.0%	1.32	Leader vs bottom half
250	80.8%	1.24	Leader vs bottom 3
300	84.9%	1.18	Bayern München vs Borussia M'gladbach

Elo Rating History — Top 6

How the top 6 teams' Elo ratings evolved across the season.

Bayern MünchenBorussia DortmundWerder BremenBayer LeverkusenFreiburgRB Leipzig

Elo Ratings — All Teams

Current Elo rating, peak Elo this season, and results. Start rating: 1500.

#	Team	Elo	Peak	Δ	GP	W	D	L
1	Bayern München	1576	1576	+76	7	5	2	0
2	Borussia Dortmund	1564	1564	+64	7	5	2	0
3	Werder Bremen	1536	1536	+36	7	3	3	1
4	Bayer Leverkusen	1534	1551	+34	7	4	1	2
5	Freiburg	1522	1554	+22	7	4	1	2
6	RB Leipzig	1513	1513	+13	7	4	0	3
7	Elversberg	1498	1531	-2	7	3	1	3
8	Mainz 05	1494	1516	-6	7	3	1	3
9	Augsburg	1494	1532	-6	7	2	3	2
10	Eintracht Frankfurt	1492	1505	-8	7	2	2	3
11	Hamburger SV	1489	1503	-11	7	3	0	4
12	Paderborn	1487	1503	-13	7	2	2	3
13	Hoffenheim	1486	1500	-14	7	3	0	4
14	Köln	1476	1513	-24	7	2	2	3
15	Union Berlin	1475	1500	-25	7	1	3	3
16	Schalke 04	1475	1504	-25	7	1	3	3
17	Stuttgart	1455	1501	-45	7	2	0	5
18	Borussia M'gladbach	1434	1500	-66	7	1	0	6

Elo Betting ROI

Backtested ROI using incremental Elo ratings — ratings computed from matches played before each bet.

Strategy	Bets	Wins	Win %	P&L	ROI
Elo Value (edge > 3%)	36	6	17%	−$57.80	-16.06%
Elo Favourite	45	21	47%	−$10.60	-2.36%
Elo Draw (>28%)	14	3	21%	+$27.20	+19.43%

Staking Plan Comparison

Same Elo bets, different stake sizing. Flat = $10 every bet. Kelly ¼ and Confidence-weighted alternatives.

Strategy	Bets	Flat P&L	Flat ROI	Kelly ¼ P&L	Kelly ¼ ROI	Conf P&L	Conf ROI
Elo Value (edge > 3%)	36	−$57.80	-16.06%	−$1.88	-65.73%	−$81.13	-21.83%
Elo Favourite	45	−$10.60	-2.36%	−$27.30	-28.82%	−$9.01	-1.95%
Elo Draw (>28%)	14	+$27.20	+19.43%	−$0.35	-4.32%	−$15.03	-11.03%

Staking Methods

Flat Stake:: Fixed $10 on every qualifying bet regardless of odds or edge.
Kelly ¼:: Stake = bankroll × (edge / (odds−1)) / 4. Uses rolling win rate as probability estimate. Grows bankroll when winning, shrinks when losing.
Confidence-weighted:: Stake = (implied prob / rolling avg implied prob) × $10. Bets more on favorites, less on longshots. Uses your previous bets' average to calculate each stake — applicable in real time.

How Elo Ratings Work

Step 1 — Starting Rating:: Every team starts at 1500. Higher Elo means a stronger team.
Step 2 — Expected Score:: E(A) = 1 / (1 + 10^(R_B − R_A) / 400). If Arsenal is rated 1600 and Wolves is 1450, Arsenal's expected score is 0.71.
Step 3 — Update After Match:: R_new = R_old + K × (S − E), where S is the actual result (1 = win, 0.5 = draw, 0 = loss) and K=32 controls sensitivity.
Step 4 — Home Advantage:: Home team gets +65 Elo boost when calculating expected scores, reflecting the statistical home advantage in football.
Step 5 — Betting:: Convert Elo difference into win/draw/loss probabilities, compare against bookmaker odds, and bet when the model spots value.

Value Scanner

No upcoming matches with odds available. Please check back later.

Bundesliga Germany Prediction Models

Flat Betting Model

Odds Range Filter

Market ROI Summary

Staking Plan Comparison

By Team Bets

PPG Model

Odds Range Filter

PPG / WPPG Betting ROI

Staking Plan Comparison

Recent Results — PPG Predictions vs Actual

Upcoming Fixtures — PPG Predictions

PPG Ratings — All Teams

PPG vs WPPG — Biggest Divergence

Worked Example — Freiburg vs Bayern München

Poisson / Correct Score

Select Match

Dixon-Coles Score Matrix — Elversberg vs Mainz 05

Derived Markets

Correct Score — Top 12

Dixon-Coles vs Plain Poisson

Dixon-Coles Betting ROI

Staking Plan Comparison

Team Attack & Defense Strength

How Poisson Predictions Work

Elo Rating

Current Elo Ratings

Elo → Win Probability Table

Elo Rating History — Top 6

Elo Ratings — All Teams

Elo Betting ROI

Staking Plan Comparison

How Elo Ratings Work

Value Scanner