ITA1 · Serie A · Match Analysis

Monza vs
Sassuolo

Model-based match probabilities derived from multi-season xG data, team strength, venue performance, recent form, Elo ratings and league context.

Analysis date18 Sep 2026

Target season2026/27

History window5 seasons

League tierTier 1

Model output

Match probabilities.

Probabilities are model estimates, not certainties or betting guarantees.

Home win

36.00%

Monza

Draw

26.01%

Away win

37.99%

Sassuolo

Expected goals 1.59 – 1.64 3.22 total

BTTS 65.41% Both teams to score

Over 1.5 84.53% 2+ total goals

Over 2.5 62.50% 3+ total goals

Score model

Most likely scorelines.

Exact-score probabilities from the Poisson + Dixon-Coles goal model.

#11:111.68%

#21:28.45%

#32:18.21%

#42:26.71%

#50:05.33%

Additional scorelines

0:25.32%

0:15.17%

2:05.02%

1:04.98%

1:34.61%

Team context

Strength and recent form.

Venue-adjusted team strength, Elo rating and recent xG performance.

Home

Monza

Elo1372.2

Overall attack1.79

Overall defense0.96

Home attack1.70

Home defense0.98

Form signal+0.919

Last 5 xGF / xGA1.87 / 0.95

Last 10 xGF / xGA1.87 / 0.95

Last 10 xGD+0.92

League statusSTABLE · from ITA1 2024/25

Away

Sassuolo

Elo1492.1

Overall attack1.78

Overall defense1.28

Away attack2.15

Away defense1.39

Form signal+0.363

Last 5 xGF / xGA1.67 / 1.31

Last 10 xGF / xGA1.67 / 1.31

Last 10 xGD+0.36

League statusSTABLE · from ITA1 2025/26

Elo difference

-119.9

Elo is one input among several and does not determine the final 1X2 probability on its own.

Head-to-head

Historical matchup context.

H2H values provide context and are not used as a standalone prediction.

H2H matches4Effective: 0.47

Average goals1.59Monza 1.21 · Sassuolo 0.38

H2H 3+ goals21.21%Historical context

H2H BTTS38.19%Model BTTS: 65.41%

H2H 2+ goals38.19%

H2H 3+ goals21.21%

H2H 4+ goals0.00%

H2H BTTS38.19%

Goal distribution

Total goals profile.

The model's probability distribution for the total number of goals.

05.33%

110.15%

222.03%

322.23%

417.91%

511.55%

66.20%

7+4.61%

0–1 goals15.47%Low total

2+ goals84.53%Over 1.5

3+ goals62.50%Over 2.5

4+ goals40.27%Over 3.5

Technical detail

How the model reached the estimate.

Detailed diagnostics are kept below the headline probabilities so the public report remains readable.

League baseline and model setup

LeagueITA1

Loaded teams27

Loaded historical matches1560

Target season2026/27

Previous seasons in history4

Season window2022/23 → 2023/24 → 2024/25 → 2025/26 → 2026/27

League tierTier 1

Historical league matches1560

xG matches40 / 1560

Effective xG matches39.40

Observed home xG1.55

Observed away xG1.43

Model home xG baseline1.55

Model away xG baseline1.43

Total league xG baseline2.98

Home-advantage weight1.000

Model configdata/models/ITA1.json

Team attack half-life240.0 days

Team defense half-life240.0 days

Attack strength weight1.000

Defense strength weight1.000

Venue prior matches0.00

DistributionPoisson + Dixon-Coles

Dixon-Coles rho-0.130 (effective -0.130)

Dixon-Coles decay0.000

Goal overdispersion0.000

Implied goal variance H / A1.59 / 1.64

Relative strengths and form diagnostics

Monza

Home attack strength1.015

Home defense weakness0.950

Base lambda home1.53

Attack form xGF level1.874

Defense form xGA level (lower is better)0.955

Opponent-adjusted attack residual+0.473

Opponent-adjusted defense residual (higher is better)+0.629

Opponent-adjustment weight0.000

Model attack signal+0.384

Model defense signal+0.535

Opponent blend0.000

Form matches 5 / 104 / 4

Last 5 xGF / xGA / xGD1.87 / 0.95 / +0.92

Last 10 xGF / xGA / xGD1.87 / 0.95 / +0.92

Sassuolo

Away attack strength1.139

Away defense weakness0.971

Base lambda away1.54

Attack form xGF level1.668

Defense form xGA level (lower is better)1.306

Opponent-adjusted attack residual+0.202

Opponent-adjusted defense residual (higher is better)+0.208

Opponent-adjustment weight0.000

Model attack signal+0.178

Model defense signal+0.184

Opponent blend0.000

Form matches 5 / 104 / 4

Last 5 xGF / xGA / xGD1.67 / 1.31 / +0.36

Last 10 xGF / xGA / xGD1.67 / 1.31 / +0.36

Historical data coverage

Monza

Total matches118

Home / away matches58 / 60

xG matches4 / 118

xG home / away coverage1 / 58 · 3 / 60

Effective total3.82

Effective attack / defense3.82 / 3.82

Effective home matches0.94

Effective home attack / defense0.94 / 0.94

Sassuolo

Total matches118

Home / away matches59 / 59

xG matches4 / 118

xG home / away coverage2 / 59 · 2 / 59

Effective total3.82

Effective attack / defense3.82 / 3.82

Effective away matches1.89

Effective away attack / defense1.89 / 1.89

Historical data coverage

2.54%

This measures usable historical evidence. It is not the probability that the prediction is correct.

Validation

Historical model performance.

Leakage-safe backtesting compares the fitted model with a league baseline over 1540 historical matches.

Positive improvement means the model performed better than the league baseline.

Total goals log-loss vs baseline+0.05%

1X2 log-loss vs baseline+1.81%

BTTS Brier vs baseline-0.07%

Over 2.5 Brier vs baseline+0.01%

Full backtest metrics

Model

MAE total goals1.2539

Exact score log-loss2.8802

Total goals Brier0.8149

Total goals log-loss1.8195

1X2 Brier0.6445

1X2 log-loss1.0666

BTTS Brier0.2523

Over 1.5 Brier0.1938

Over 2.5 Brier0.2495

Over 3.5 Brier0.1873

League baseline

MAE total goals1.2543

Exact score log-loss2.9055

Total goals Brier0.8149

Total goals log-loss1.8204

1X2 Brier0.6583

1X2 log-loss1.0863

BTTS Brier0.2522

Over 1.5 Brier0.1939

Over 2.5 Brier0.2495

Over 3.5 Brier0.1875

Calibration diagnostics

Home win · ECE 9.17%

10- 20%  n=1    avg= 18.3% actual=  0.0%
20- 30%  n=7    avg= 25.5% actual= 71.4%
30- 40%  n=432  avg= 38.4% actual= 21.3%
40- 50%  n=1097 avg= 42.3% actual= 48.1%
50- 60%  n=3    avg= 52.5% actual= 66.7%

Draw · ECE 1.78%

20- 30%  n=1533 avg= 29.0% actual= 27.3%
30- 40%  n=7    avg= 31.2% actual= 14.3%

Away win · ECE 13.48%

20- 30%  n=869  avg= 28.3% actual= 18.4%
30- 40%  n=662  avg= 31.6% actual= 49.8%
40- 50%  n=6    avg= 45.5% actual= 33.3%
50- 60%  n=3    avg= 53.6% actual= 66.7%

BTTS · ECE 4.90%

40- 50%  n=5    avg= 48.4% actual= 60.0%
50- 60%  n=1522 avg= 55.0% actual= 50.1%
60- 70%  n=13   avg= 63.0% actual= 61.5%

Over 2.5 · ECE 2.03%

30- 40%  n=3    avg= 39.5% actual= 33.3%
40- 50%  n=1382 avg= 49.3% actual= 47.3%
50- 60%  n=148  avg= 51.0% actual= 49.3%
60- 70%  n=7    avg= 63.0% actual= 57.1%

Walk-forward - latest periods

Period	N	Model 1X2 LL	Baseline 1X2 LL	Improvement
2025-08	20	1.0604	1.0893	+2.65%
2025-09	30	1.0695	1.0886	+1.75%
2025-10	40	1.0919	1.1084	+1.49%
2025-11	39	1.0768	1.0934	+1.52%
2025-12	37	1.0641	1.0757	+1.07%
2026-01	58	1.0680	1.1016	+3.05%
2026-02	40	1.0634	1.0959	+2.97%
2026-03	36	1.0330	1.0535	+1.94%
2026-04	40	1.0600	1.0887	+2.63%
2026-05	40	1.0895	1.1124	+2.06%
2026-08	20	1.0284	1.0765	+4.47%
2026-09	20	1.0778	1.0812	+0.31%

Football is data.

NILYTIX turns match data into transparent model probabilities and measurable analytical signals.

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Monza vsSassuolo

Match probabilities.

Most likely scorelines.

Strength and recent form.

Monza

Sassuolo

Historical matchup context.

Total goals profile.

How the model reached the estimate.

Monza

Sassuolo

Monza

Sassuolo

Historical model performance.

Home win · ECE 9.17%

Draw · ECE 1.78%

Away win · ECE 13.48%

BTTS · ECE 4.90%

Over 2.5 · ECE 2.03%

Football is data.

Monza vs
Sassuolo