ITA1 · Serie A · Match Analysis

Juventus vs
Lazio

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

Analysis date18 Oct 2026
Target season2026/27
History window5 seasons
League tierTier 1
Model output

Match probabilities.

Probabilities are model estimates, not certainties or betting guarantees.

Draw
26.23%
X
Away win
16.59%
Lazio
Expected goals 1.70 - 0.81 2.51 total
BTTS 46.68% Both teams to score
Over 1.5 72.68% 2+ total goals
Over 2.5 45.90% 3+ total goals
Score model

Most likely scorelines.

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

#11:012.59%
#21:112.40%
#32:011.69%
#42:19.52%
#50:09.30%
Additional scorelines
3:06.61%
0:15.43%
3:15.38%
1:24.57%
2:23.87%
Team context

Strength and recent form.

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

Home

Juventus

Elo1595.7
Overall attack1.68
Overall defense0.82
Home attack1.81
Home defense0.71
Form signal+1.050
Last 5 xGF / xGA1.53 / 0.54
Last 10 xGF / xGA1.70 / 0.55
Last 10 xGD+1.15
League statusSTABLE · from ITA1 2025/26
Away

Lazio

Elo1576.9
Overall attack1.31
Overall defense1.20
Away attack1.17
Away defense1.20
Form signal+0.317
Last 5 xGF / xGA1.80 / 1.38
Last 10 xGF / xGA1.52 / 1.38
Last 10 xGD+0.14
League statusSTABLE · from ITA1 2025/26
Elo difference
+18.8

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 matches8Effective: 2.19
Average goals2.32Juventus 1.14 · Lazio 1.18
H2H 3+ goals40.67%Historical context
H2H BTTS54.53%Model BTTS: 46.68%
H2H 2+ goals57.52%
H2H 3+ goals40.67%
H2H 4+ goals33.72%
H2H BTTS54.53%
Goal distribution

Total goals profile.

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

09.30%
118.02%
226.78%
321.42%
413.45%
56.75%
62.83%
7+1.45%
0-1 goals27.32%Low total
2+ goals72.68%Over 1.5
3+ goals45.90%Over 2.5
4+ goals24.48%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 matches1580
Target season2026/27
Previous seasons in history4
Season window2022/23 → 2023/24 → 2024/25 → 2025/26 → 2026/27
League tierTier 1
Historical league matches1580
xG matches1580 / 1580
Effective xG matches798.37
Observed home xG1.37
Observed away xG1.17
Model home xG baseline1.36
Model away xG baseline1.18
Total league xG baseline2.54
Home-advantage weight0.875
Model configdata/models/ITA1.json
Team attack half-life240.0 days
Team defense half-life240.0 days
Attack strength weight1.050
Defense strength weight1.081
Venue prior matches3.31
DistributionPoisson + Dixon-Coles
Dixon-Coles rho-0.106 (effective -0.106)
Dixon-Coles decay0.034
Goal overdispersion0.000
Implied goal variance H / A1.70 / 0.81
Relative strengths and form diagnostics

Juventus

Home attack strength1.251
Home defense weakness0.697
Base lambda home1.55
Attack form xGF level1.590
Defense form xGA level (lower is better)0.540
Opponent-adjusted attack residual+0.287
Opponent-adjusted defense residual (higher is better)+0.699
Opponent-adjustment weight0.000
Model attack signal+0.319
Model defense signal+0.731
Opponent blend0.000
Form matches 5 / 105 / 10
Last 5 xGF / xGA / xGD1.53 / 0.54 / +1.00
Last 10 xGF / xGA / xGD1.70 / 0.55 / +1.15

Lazio

Away attack strength1.001
Away defense weakness0.908
Base lambda away0.80
Attack form xGF level1.700
Defense form xGA level (lower is better)1.382
Opponent-adjusted attack residual+0.372
Opponent-adjusted defense residual (higher is better)-0.165
Opponent-adjustment weight0.000
Model attack signal+0.429
Model defense signal-0.111
Opponent blend0.000
Form matches 5 / 105 / 10
Last 5 xGF / xGA / xGD1.80 / 1.38 / +0.41
Last 10 xGF / xGA / xGD1.52 / 1.38 / +0.14
Historical data coverage

Juventus

Total matches158
Home / away matches79 / 79
xG matches158 / 158
xG home / away coverage79 / 79 · 79 / 79
Effective total31.57
Effective attack / defense31.57 / 31.57
Effective home matches15.66
Effective home attack / defense15.66 / 15.66

Lazio

Total matches158
Home / away matches79 / 79
xG matches158 / 158
xG home / away coverage79 / 79 · 79 / 79
Effective total31.59
Effective attack / defense31.59 / 31.59
Effective away matches15.71
Effective away attack / defense15.71 / 15.71
Historical data coverage
95.96%

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 1560 historical matches.

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

Total goals log-loss vs baseline+0.63%
1X2 log-loss vs baseline+9.07%
BTTS Brier vs baseline+0.37%
Over 2.5 Brier vs baseline+1.34%
Full backtest metrics
Model
MAE total goals1.2171
Exact score log-loss2.7695
Total goals Brier0.8119
Total goals log-loss1.8027
1X2 Brier0.5899
1X2 log-loss0.9879
BTTS Brier0.2496
Over 1.5 Brier0.1909
Over 2.5 Brier0.2463
Over 3.5 Brier0.1841
League baseline
MAE total goals1.2442
Exact score log-loss2.8998
Total goals Brier0.8136
Total goals log-loss1.8143
1X2 Brier0.6583
1X2 log-loss1.0864
BTTS Brier0.2505
Over 1.5 Brier0.1925
Over 2.5 Brier0.2497
Over 3.5 Brier0.1866
Calibration diagnostics
Home win · ECE 2.51%
 0- 10%  n=22   avg=  8.3% actual=  9.1%
10- 20%  n=177  avg= 15.9% actual= 15.8%
20- 30%  n=282  avg= 25.2% actual= 25.5%
30- 40%  n=333  avg= 35.2% actual= 33.6%
40- 50%  n=297  avg= 44.7% actual= 48.1%
50- 60%  n=218  avg= 54.8% actual= 51.8%
60- 70%  n=161  avg= 64.5% actual= 70.8%
70- 80%  n=59   avg= 73.8% actual= 79.7%
80- 90%  n=11   avg= 83.1% actual= 63.6%
Draw · ECE 1.65%
 0- 10%  n=1    avg=  8.3% actual=  0.0%
10- 20%  n=94   avg= 17.1% actual= 21.3%
20- 30%  n=1059 avg= 26.5% actual= 25.3%
30- 40%  n=406  avg= 31.5% actual= 33.7%
Away win · ECE 3.79%
 0- 10%  n=73   avg=  7.7% actual=  6.8%
10- 20%  n=308  avg= 15.0% actual= 12.3%
20- 30%  n=368  avg= 25.0% actual= 22.0%
30- 40%  n=343  avg= 34.6% actual= 30.6%
40- 50%  n=208  avg= 44.6% actual= 53.4%
50- 60%  n=160  avg= 54.3% actual= 53.8%
60- 70%  n=74   avg= 63.6% actual= 70.3%
70- 80%  n=25   avg= 73.6% actual= 76.0%
80- 90%  n=1    avg= 83.8% actual=  0.0%
BTTS · ECE 1.92%
30- 40%  n=64   avg= 37.9% actual= 45.3%
40- 50%  n=823  avg= 45.9% actual= 48.4%
50- 60%  n=637  avg= 53.6% actual= 53.1%
60- 70%  n=36   avg= 61.8% actual= 58.3%
Over 2.5 · ECE 2.49%
20- 30%  n=17   avg= 28.1% actual= 23.5%
30- 40%  n=300  avg= 36.9% actual= 40.0%
40- 50%  n=705  avg= 44.9% actual= 47.1%
50- 60%  n=455  avg= 53.9% actual= 51.6%
60- 70%  n=78   avg= 63.4% actual= 60.3%
70- 80%  n=5    avg= 71.5% actual= 80.0%
Walk-forward - latest periods
PeriodNModel 1X2 LLBaseline 1X2 LLImprovement
2025-09300.98251.0882+9.71%
2025-10401.04911.1060+5.14%
2025-11391.01411.0922+7.15%
2025-12371.06391.0763+1.15%
2026-01580.93651.0986+14.75%
2026-02400.95071.0931+13.03%
2026-03360.98941.0574+6.43%
2026-04401.00261.0882+7.87%
2026-05401.10071.1080+0.66%
2026-08200.77771.0805+28.03%
2026-09301.02371.0700+4.33%
2026-10100.89091.0354+13.96%

Football is data.

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