Home / Leagues / Latvia / Virsliga / 2017
‹ ›

2017 Virsliga Season

84 games

Final

Champion

Spartaks Jurmala

46 points

Relegated

No relegation

Biggest Overachiever

Spartaks Jurmala

7.84 points above expected

46 points · 38.16 expected points

Biggest Disappointment

Metta

7.31 points below expected

15 points · 22.31 expected points

League Table

The final standings for the season. vsSim shows actual points minus the simulation's mean — positive means the team overachieved against the model, negative means they underperformed.

# Team GP W D L Pts GF GA GD SimPts vsSim
1 Spartaks Jurmala 24 14 4 6 46 36 26 +10 38.16 +7.84
2 Liepaja 24 11 4 9 37 32 25 +7 34.41 +2.59
3 Riga FC 24 10 7 7 37 28 20 +8 35.34 +1.66
4 Ventspils 24 9 8 7 35 32 22 +10 39.50 -4.50
5 FK RFS 24 11 2 11 35 29 31 -2 28.96 +6.04
6 Jelgava 24 8 5 11 29 22 30 -8 34.69 -5.69
7 Metta 24 3 6 15 15 21 46 -25 22.31 -7.31

Top Overachievers & Disappointments

Teams that most beat — or most fell short of — their simulated point projections. A positive vsSim means the team accumulated more points than the model expected on average; a negative one means fewer.

Biggest Overachievers

# Team Actual Sim vsSim
1 Spartaks Jurmala 46 38.16 +7.84
2 FK RFS 35 28.96 +6.04
3 Liepaja 37 34.41 +2.59
4 Riga FC 37 35.34 +1.66
5 Ventspils 35 39.50 -4.50

Biggest Disappointments

# Team Actual Sim vsSim
1 Metta 15 22.31 -7.31
2 Jelgava 29 34.69 -5.69
3 Ventspils 35 39.50 -4.50
4 Riga FC 37 35.34 +1.66
5 Liepaja 37 34.41 +2.59

Top Streaks

Ranked by model unlikelihood — the product of pregame W/D/L probabilities over the games in each team's streak. A short streak by a poor team can outrank a longer one by a strong team since the poor team's per-game probabilities were lower going in. Unbeaten counts consecutive wins or ties; Winless counts consecutive losses or ties.

Most Unlikely Winning Streaks

# Team Games Dates Probability
1 FK RFS 4 Aug 26 – Sep 27 1 in 120
2 Spartaks Jurmala 4 Mar 18 – Apr 29 1 in 33
3 Ventspils 4 Jul 24 – Aug 9 1 in 26
4 Riga FC 3 Aug 25 – Sep 24 1 in 19
5 Liepaja 3 May 7 – May 21 1 in 16

Most Unlikely Losing Streaks

# Team Games Dates Probability
1 Ventspils 3 May 27 – Jun 22 1 in 32
2 FK RFS 4 Apr 22 – May 12 1 in 19
3 Riga FC 3 Jul 24 – Aug 5 1 in 17
4 Jelgava 3 Jul 31 – Aug 13 1 in 14
5 Metta 4 Jul 24 – Aug 13 1 in 11

Most Unlikely Unbeaten Streaks

# Team Games Dates Probability
1 Spartaks Jurmala 12 Mar 18 – Aug 5 1 in 130
2 FK RFS 5 Aug 21 – Sep 27 1 in 25
3 Riga FC 5 May 21 – Jun 22 1 in 9
4 Liepaja 4 Jul 24 – Aug 21 1 in 7
5 Jelgava 4 Oct 14 – Nov 4 1 in 7

Most Unlikely Winless Streaks

# Team Games Dates Probability
1 Jelgava 8 Jun 22 – Sep 10 1 in 71
2 Ventspils 6 May 7 – Jun 22 1 in 46
3 Metta 8 Apr 16 – Jun 17 1 in 11
4 Riga FC 4 May 13 – Jun 2 1 in 9
5 Spartaks Jurmala 3 May 27 – Jun 22 1 in 5

Position Race

Each team’s league position across the whole season. Colored teams are the season’s headline finishers — champion, promoted, and relegated; everyone else is gray as context.

Recent Form

Each team’s last 5 games, hottest first. Form shows the results (navy win, gray draw, red loss, newest on the right); the bars show Elo change over the window, points taken, points expected from the odds, and actual minus expected. Beside each team: current Elo and season points.

Team Elo Season Pts Form Elo Δ (5) Points (5) Expected (5) Actual − Exp
Spartaks Jurmala 1311 46 +4 9 7.1 +1.9
Liepaja 1292 37 -7 7 7.7 -0.7
Riga FC 1308 37 +4 7 7.9 -0.9
Ventspils 1341 35 -6 7 8.1 -1.1
FK RFS 1245 35 -20 3 6.6 -3.6
Jelgava 1267 29 +6 8 6.8 +1.2
Metta 1143 15 +0 4 4.3 -0.3

Head-to-Head

Each cell shows a team's record in that matchup (row vs column, formatted W-D-L) with the model's expected points on the line below. Navy-tinted cells mean the team beat the model's expectations by more than one point in that matchup; gold-tinted cells mean they fell short by the same margin.

Beat expectations Fell short Within expectations
Team FR JEL LIE MET RIG SJ VEN
FK RFS —
1-1-2
4.85
2-0-2
4.69
2-0-2
6.79
1-1-2
4.61
3-0-1
4.60
2-0-2
4.09
Jelgava
2-1-1
6.25
 —
1-0-3
5.61
2-2-0
7.42
0-1-3
5.33
1-1-2
5.18
2-0-2
4.83
Liepaja
2-0-2
6.42
3-0-1
5.48
 —
3-0-1
7.47
2-0-2
5.29
0-1-3
5.27
1-3-0
4.85
Metta
2-0-2
4.34
0-2-2
3.74
1-0-3
3.69
 —
0-2-2
3.67
0-0-4
3.56
0-2-2
3.34
Riga FC
2-1-1
6.50
3-1-0
5.76
2-0-2
5.80
2-2-0
7.49
 —
0-1-3
5.43
1-2-1
5.04
Spartaks Jurmala
1-0-3
6.50
2-1-1
5.92
3-1-0
5.82
4-0-0
7.61
3-1-0
5.66
 —
1-1-2
5.24
Ventspils
2-0-2
7.04
2-0-2
6.27
0-3-1
6.25
2-2-0
7.86
1-2-1
6.06
2-1-1
5.85
 —

Points vs. Goals Scored, Allowed, and Differential

Points plotted against goals scored, allowed, and differential. Use the buttons to switch views; hover a team for exact values. The table gives each fit's R² and slope (the change in points per 10 goals).

Fit Metrics

R² Slope
Scored 0.79 +15.5
Allowed 0.68 -9.2
Differential 0.86 +6.8

Scoreline Distribution

Percentage of games ending with each combination of team-goals (rows) and opponent-goals (columns). The diagonal shows draws; cells below the diagonal are wins from the row team's perspective, cells above are losses. Marginal totals on the right and bottom show how often each goal count occurred regardless of opponent. Use the picker to switch between the league-wide view and any individual team.

↓ Scored | Allowed →012345+Total
07.14%11.90%8.33%3.57%1.19%—32.14%
111.90%10.71%6.55%4.17%——33.33%
28.33%6.55%3.57%1.19%1.79%0.60%22.02%
33.57%4.17%1.19%———8.93%
41.19%—1.79%———2.98%
5+——0.60%———0.60%
Total32.14%33.33%22.02%8.93%2.98%0.60%100%

Summary Statistics

Scored Allowed Difference
Mean 1.19 1.19 +0.00
SD 1.11 1.11 1.64
CV 0.93 0.93 —
Max 5 5 +4
Min 0 0 -4

Games Played: 84

↓ Scored | Allowed →012345+Total
0—16.67%12.50%4.17%——33.33%
116.67%8.33%4.17%4.17%——33.33%
24.17%8.33%——4.17%—16.67%
34.17%4.17%4.17%———12.50%
4——4.17%———4.17%
5+———————
Total25.00%37.50%25.00%8.33%4.17%—100%

Summary Statistics

Scored Allowed Difference
Mean 1.21 1.29 -0.08
SD 1.18 1.08 1.64
CV 0.98 0.84 —
Max 4 4 +3
Min 0 0 -3

Games Played: 24

↓ Scored | Allowed →012345+Total
04.17%12.50%—12.50%——29.17%
120.83%12.50%16.67%4.17%——54.17%
24.17%4.17%4.17%———12.50%
3—4.17%————4.17%
4———————
5+———————
Total29.17%33.33%20.83%16.67%——100%

Summary Statistics

Scored Allowed Difference
Mean 0.92 1.25 -0.33
SD 0.78 1.07 1.46
CV 0.85 0.86 —
Max 3 3 +2
Min 0 0 -3

Games Played: 24

↓ Scored | Allowed →012345+Total
04.17%16.67%8.33%———29.17%
18.33%12.50%8.33%4.17%——33.33%
28.33%12.50%————20.83%
34.17%8.33%————12.50%
4———————
5+——4.17%———4.17%
Total25.00%50.00%20.83%4.17%——100%

Summary Statistics

Scored Allowed Difference
Mean 1.33 1.04 +0.29
SD 1.27 0.81 1.55
CV 0.96 0.77 —
Max 5 3 +3
Min 0 0 -2

Games Played: 24

↓ Scored | Allowed →012345+Total
08.33%12.50%16.67%4.17%4.17%—45.83%
14.17%8.33%—12.50%——25.00%
24.17%—8.33%4.17%4.17%4.17%25.00%
3—4.17%————4.17%
4———————
5+———————
Total16.67%25.00%25.00%20.83%8.33%4.17%100%

Summary Statistics

Scored Allowed Difference
Mean 0.88 1.92 -1.04
SD 0.95 1.38 1.52
CV 1.08 0.72 —
Max 3 5 +2
Min 0 0 -4

Games Played: 24

↓ Scored | Allowed →012345+Total
012.50%8.33%16.67%———37.50%
112.50%8.33%4.17%———25.00%
28.33%8.33%8.33%———25.00%
38.33%—————8.33%
44.17%—————4.17%
5+———————
Total45.83%25.00%29.17%———100%

Summary Statistics

Scored Allowed Difference
Mean 1.17 0.83 +0.33
SD 1.17 0.87 1.66
CV 1.00 1.04 —
Max 4 2 +4
Min 0 0 -2

Games Played: 24

↓ Scored | Allowed →012345+Total
04.17%8.33%—4.17%4.17%—20.83%
18.33%12.50%4.17%4.17%——29.17%
220.83%12.50%————33.33%
3—8.33%4.17%———12.50%
4——4.17%———4.17%
5+———————
Total33.33%41.67%12.50%8.33%4.17%—100%

Summary Statistics

Scored Allowed Difference
Mean 1.50 1.08 +0.42
SD 1.10 1.10 1.69
CV 0.74 1.02 —
Max 4 4 +2
Min 0 0 -4

Games Played: 24

↓ Scored | Allowed →012345+Total
016.67%8.33%4.17%———29.17%
112.50%12.50%8.33%———33.33%
28.33%—4.17%4.17%4.17%—20.83%
38.33%—————8.33%
44.17%—4.17%———8.33%
5+———————
Total50.00%20.83%20.83%4.17%4.17%—100%

Summary Statistics

Scored Allowed Difference
Mean 1.33 0.92 +0.42
SD 1.24 1.14 1.59
CV 0.93 1.24 —
Max 4 4 +4
Min 0 0 -2

Games Played: 24

Season Summary

Every team's regular-season finish compared against 100,000 simulations. Click any column header to sort. Luck is the team's actual points minus the sim's mean — positive means the team beat the model. Percentile is where the actual result fell in the team's sim distribution (e.g. 90% = the team did this well or better in only 10% of sims).

Team Elo Points Avg Luck Percentile Min 5th Q1 Median Q3 95th Max
Ventspils 1341 35 39.50 -4.50 23.0% 23 29 36 40 44 48 57
Spartaks Jurmala 1311 46 38.16 +7.84 88.0% 20 24 33 38 44 48 52
Riga FC 1308 37 35.34 +1.66 64.0% 12 23 32 34 40 46 50
Liepaja 1292 37 34.41 +2.59 72.0% 23 25 30 33 39 46 56
Jelgava 1267 29 34.69 -5.69 19.0% 18 24 30 34 39 45 49
FK RFS 1245 35 28.96 +6.04 86.0% 14 18 23 29 33 38 43
Metta 1143 15 22.31 -7.31 8.0% 11 14 19 22 26 31 38

Point Totals in Context

How did each team's actual results compare to their simulated points, Elo rating, average opponent Elo, and percentile within simulated outcomes? Use the buttons to switch between views. In each chart, dashed crosshairs at the league means split the plot into four quadrants: pastel green (team did well, model agreed), pastel red (team did poorly, model agreed), and pastel yellow (model and reality disagreed).

Home-Field Advantage Edge

The simulation applies the same home-field boost to every game across the league, so the per-game home win probabilities bake in the model's idea of HFA. For each team, we sum expected points at home and compare to actual home points, and the same on the road. The bar shows (home points above expected) minus (away points above expected). A tall positive bar means the team's home/road split exceeded what the model predicted — a real fortress effect. A tall negative bar means the reverse: they played worse at home or better on the road than expected.

Season Trends

How each metric evolved across the season - Elo rating, actual and projected points, title/promotion probability, and relegation probability - day by day.

Edges & Scoring

How these are measured

The navy dot is the actual value, the gold line and key are the model's reference where one applies, and the slate ticks are the cutoffs between read labels listed under each metric.

Home Edge
Share of matches won by the home team minus the share won by the away team.
No Edge: under 2.5% * Slight Edge: 2.5% to 6% * Clear Edge: 6% to 11% * Strong Edge: 11% and up.
Elo Value
Number of Elo rating points one goal is worth. A team this many Elo points better than another is expected to win by one goal on a neutral field.
Scoring Tilt
Average home goals minus average away goals per match. The gold line is the home goals edge implied by the scoring value of an Elo point and the home-field Elo bonus.
Road-Tilted: under -0.2 * Neutral: -0.2 to 0.5 * Home-Tilted: 0.5 to 1.2 * Strong Home: 1.2 and up.
Home Edge
HomeDrawAway
-2.38%
No Edge
38.10%21.43%40.48%
Elo Value
Home Edge: -8.27 Elo pts.
171 Elo
0.006 goals per Elo point
0500
Scoring Tilt
Expected
+0.07 goals
Neutral
-2-0.05+2

Title Race

How these are measured

The navy dot is the actual value, the gold line and key are the model's reference where one applies, and the slate ticks are the cutoffs between read labels listed under each metric.

Title-Race Openness
Effective number of teams with a live shot at finishing 1st, from the simulation. 1 means the favorite is a near-lock; larger means a wide-open race. Equal to 1 divided by the sum of squared title odds.
One-Team Race: under 2 * Top-Heavy: 2 to 3 * Open: 3 to 4 * Wide Open: 4 and up.
Champion Preseason Odds
Preseason probability that the eventual champion would finish 1st, from the simulation. The dot marks their rank across all teams, from longshot to favorite.
Preseason Favorite: 1st * Among the Favorites: 2nd * Middle of the Pack: 3rd to 4th * Longshot: 5th or lower.
Title Margin
Points-per-game gap between the champion and the runner-up. Shown per game so it reads the same across long and short seasons. The gold line is the winning margin the model expected, so a dot to the right means a more one-sided race than projected. A title won on goal difference shows 0.00.
Photo Finish: under 0.09 * Tight Race: 0.09 to 0.16 * Comfortable: 0.16 to 0.3 * Runaway: 0.3 and up.
Title-Race Openness
4.4
Wide Open
12347
Champion Preseason Odds
33%
Spartaks Jurmala, 1st of 7
LongshotFavorite
Title Margin
Expected
0.38/gm
Runaway
00.190.5/gm

Simulation-Based Surprises

How these are measured

The navy dot is the actual value, the gold line and key are the model's reference where one applies, and the slate ticks are the cutoffs between read labels listed under each metric.

Luck Spread
Standard deviation of the gap between each team's actual points and their simulated average points. The gold line is the spread the model expected from chance alone.
As Expected: under 5.17 * Some Luck: 5.17 to 7.76 * Lucky: 7.76 to 10.34 * Wild Swing: 10.34 and up.
Average Finish Error
Average gap between where each team was projected to finish and where they actually finished in the table.
Pinpoint: under 1.04 * Close: 1.04 to 1.56 * Off: 1.56 to 2.09 * Way Off: 2.09 and up.
Biggest Overachiever
The team that finished highest inside its own range of simulated outcomes. The gold line is where the top team in a league of 7 typically lands.
Biggest Underachiever
The team that finished lowest inside its own range of simulated outcomes. The gold line is where the bottom team in a league of 7 typically lands.
Season Outliers
Number of teams that finished above the 95th or below the 5th percentile of their own simulated range. Even a well-calibrated model expects about 10% of teams in the extremes.
Minimal Outliers: under 7 * As Expected: 7 to 11.2 * Several Outliers: 11.2 to 15.4 * Many Outliers: 15.4 and up.
Luck Spread
Expected
5.52 points
Some Luck
06.4616
Average Finish Error
Expected
1.43
Close
01.303
Biggest Overachiever
Expected 92.86%
88.00%
Spartaks Jurmala
50100
Biggest Underachiever
Expected
8.00%
Metta
07.14%50
Season Outliers
Expected
0 of 7
Minimal Outliers
00.77

Parity

How these are measured

The navy dot is the actual value, the gold line and key are the model's reference where one applies, and the slate ticks are the cutoffs between read labels listed under each metric.

Gini Index
How evenly points were spread across the league. Lower means a tight, balanced table; higher means a few teams ran off with most of the points.
Balanced: under 0.24 * Even: 0.24 to 0.27 * Top-Heavy: 0.27 to 0.28 * Lopsided: 0.28 and up.
Noll-Scully
Take a league where every match is a coin flip and nobody is better than anybody. You would still get a table, with a leader and a bottom club, purely by luck, and you can work out how far apart that table would be. This is the real table measured against it. At 1.00 the season was no more spread out than pure chance; at 2.00 it was twice as spread out, which takes genuine differences in quality. Dividing by the luck figure is what makes seasons of different lengths comparable, because a short season spreads a table by luck alone far more than a long one does. Elo SD in the corner is the same question in rating points: how far apart the teams finished on the ratings.
Coin-Flip Parity: under 2.55 * Moderate Separation: 2.55 to 2.8 * Strong Separation: 2.8 to 2.95 * Wide Separation: 2.95 and up.
Interquartile Edge
Chance the team at the 75th percentile of Elo would beat the team at the 25th percentile on a neutral field. Higher means a bigger gap between the upper and lower half of the table.
Even: under 82% * Slight Edge: 82% to 86.5% * Clear Edge: 86.5% to 91.5% * Wide Edge: 91.5% and up.
Best vs. Worst
Chance the top-rated team would beat the bottom-rated team on a neutral field. The gold line is how large that gap tends to be in a league of this size; a dot to the right flags an unusually dominant or unusually weak team.
Even: under 97% * Clear Edge: 97% to 98% * Strong Edge: 98% to 99% * Dominant: 99% and up.
Close Games
Share of matches decided by 1 goal or fewer, draws included. The gold line is how many close games the matchups and the scoring value of an Elo point predict.
Few: under 49% * Some Drama: 49% to 51% * Frequent: 51% to 57.5% * Very Frequent: 57.5% and up.
Blowouts
Share of matches decided by 3 goals or more. The gold line is how many routs the matchups and the scoring model predict.
Rare: under 21% * Occasional: 21% to 27.5% * Frequent: 27.5% to 32% * Very Frequent: 32% and up.
Gini Index
0.14
Balanced
00.240.5
Noll-Scully
Elo SD: 64.97
1.45
Coin-Flip Parity
12.552.953.5
Interquartile Edge
58%
Even
50%82%100%
Best vs. Worst
Baseline
76%
Even
50%75%100%
Close Games
Expected
61%
Very Frequent
0%64%100%
Blowouts
Expected
11%
Rare
0%13%100%

Predictability

How these are measured

The navy dot is the actual value, the gold line and key are the model's reference where one applies, and the slate ticks are the cutoffs between read labels listed under each metric.

Brier Score
How close the pregame probabilities landed to the actual result, averaged over the season. Confident, correct calls are rewarded most; confident misses are punished most. Lower is better; since draws are possible, a score under 0.66 beats guessing the base rate.
Highly Predictable: under 0.56 * Predictable: 0.56 to 0.62 * Hard to Predict: 0.62 to 0.66 * Coin-Flip: 0.66 and up.
Matchup Imbalance
How lopsided the matchups were on paper, averaging the gap between the two win probabilities over their sum. 0 means every match was a toss-up; 1 means every match was a heavy favorite against a big underdog.
Very Even: under 0.51 * Slight Separation: 0.51 to 0.55 * Notable Separation: 0.55 to 0.58 * Lopsided: 0.58 and up.
Strangeness
How wild the final table was versus what the model expected. A value of 1 means teams landed about one standard deviation from their projections on average. Above 1 is a stranger season; below 1 hugged the projections.
Very Predictable: under 0.8 * As Expected: 0.8 to 1.1 * Wilder Than Modeled: 1.1 to 1.4 * Chaotic: 1.4 and up.
Repeatability
How closely the final table order matched the preseason Elo order, by Spearman rank correlation. Higher means last season's ratings strongly predicted this season's finish. Shows N/A for an inaugural season.
Weak Carryover: under 0.62 * Some Carryover: 0.62 to 0.76 * Strong Carryover: 0.76 to 0.83 * Near-Lock: 0.83 and up.
Upset Rate
Share of matches the underdog won. The gold line is how often the model expected underdogs to win; a dot to the right means upsets ran hotter than expected.
Chalky: under 13.5% * As Expected: 13.5% to 16.5% * Upset-Prone: 16.5% to 18.5% * Very Upset-Prone: 18.5% and up.
Clear Favorite Upset Rate
Share of matches the underdog won, counting only games with a clear favorite (at least 60% likely to win once a draw is set aside). The gold line is how often the model expected these favorites to slip.
Solid Favorites: under 12% * As Expected: 12% to 14% * Shaky Favorites: 14% to 15% * Very Shaky: 15% and up.
Brier Score
Expected
0.63
Hard to Predict
00.632
Matchup Imbalance
0.21
Very Even
00.5
Strangeness
Expected
0.78
Very Predictable
01.002
Repeatability
0.16
Weak Carryover
00.620.761
Upset Rate
Expected
37%
Very Upset-Prone
0%31%50%
Clear Favorite Upset Rate
Expected
22%
Very Shaky
0%26%50%

Calibration

How these are measured

The navy dot is the actual value, the gold line and key are the model's reference where one applies, and the slate ticks are the cutoffs between read labels listed under each metric.

Probability calibration
An all-in-one chi-square test of the model's probabilities. Matches are grouped by how confident the model was, and within each group the predicted and actual counts of home wins, draws, and away wins are compared. The p-value is plotted; above 0.05 means well-calibrated.
Miscalibrated: under 0.05 * Borderline: 0.05 to 0.1 * Well Calibrated: 0.1 to 0.5 * Excellent: 0.5 and up.
Calibration slope
Checks whether the spread of the probabilities is right. Each probability is turned into log-odds and a line is fit predicting the actual results. A slope of 1.00 is perfect; below 1 is overconfidence (favorites lost more than their odds implied); above 1 is under-confidence.
Overconfident: under 0.85 * Calibrated: 0.85 to 1.15 * Underconfident: 1.15 to 1.3 * Very Underconfident: 1.3 and up.
Calibration error (ECE)
The average gap between the model's stated chances and how often the predicted result actually happened. Smaller is better. The gold line is the noise ceiling, the error luck alone can produce even with perfect probabilities; below it, the model's error is no larger than chance.
Well Within Noise: under 0.18 * Near Noise Ceiling: 0.18 to 0.27 * Above Noise: 0.27 to 0.36 * Well Above Noise: 0.36 and up.
Probability calibration
0.61
Excellent
0.010.050.10.51
Calibration slope
Ideal
1.06
Calibrated
0.51.001.5
Calibration error (ECE)
Noise ceiling
0.124
Well Within Noise
00.1800.4

Final Table Odds

Each cell is the probability — across 100,000 simulations — that the team (row) finished at that position (column). Rows are sorted by actual finish (champion at top, bottom of the table at the bottom).

Team1234567
Spartaks Jurmala33.00%23.00%11.00%11.00%14.00%5.00%3.00%
Liepaja13.00%7.00%22.00%17.00%25.00%12.00%4.00%
Riga FC15.00%16.00%19.00%22.00%16.00%6.00%6.00%
Ventspils23.00%37.00%20.00%7.00%7.00%5.00%1.00%
FK RFS—6.00%11.00%19.00%15.00%32.00%17.00%
Jelgava16.00%10.00%16.00%22.00%16.00%17.00%3.00%
Metta—1.00%1.00%2.00%7.00%23.00%66.00%

Points Required Per Position

The empirical CDF of simulated point totals per finishing position. Each curve shows, for one position (1st, 2nd, ..., last), the spread of point totals teams accumulated across simulations. Reading the curve at the 50% mark gives the median points typically needed to finish at that position. Steep curves mean the position is tightly clustered around a particular point range; shallow curves mean the position came with a wide variety of point totals.

Next-Season Status

Across 100,000 regular-season simulations, the probability of each team's next-season status. Columns appear in best-to-worst outcome order: promotion-positive on the left, relegation-positive on the right. Cells with darker shading indicate higher likelihood.

Team Same level Qualified for relegation playoff
FK RFS 83.00% 17.00%
Jelgava 97.00% 3.00%
Liepaja 96.00% 4.00%
Metta 34.00% 66.00%
Riga FC 94.00% 6.00%
Spartaks Jurmala 97.00% 3.00%
Ventspils 99.00% 1.00%

Overall Game Log

Summary of every completed game this season. Sort any column by clicking its header.

Date Opponent ScoreExcitement Pre Elo Opp Elo Win % Tie % Loss %Proj. Margin Elo Δ Points
2017-03-11 Metta W 2-032.3 1321 1191 56.09% 20.83% 23.08%+0.71 +5.6 3
2017-03-11 @ Ventspils L 0-232.3 1191 1321 23.08% 20.83% 56.09%-0.71 -5.6 0
2017-03-11 Riga FC L 0-141.7 1277 1288 37.03% 22.72% 40.25%-0.11 -4.5 0
2017-03-11 @ Liepaja W 1-041.7 1288 1277 40.25% 22.72% 37.03%+0.11 +4.5 3
2017-03-12 Spartaks Jurmala W 2-150.2 1239 1285 32.35% 22.48% 45.17%-0.32 +5.0 3
2017-03-12 @ FK RFS L 1-250.2 1285 1239 45.17% 22.48% 32.35%+0.32 -5.0 0
2017-03-17 Liepaja L 1-250.1 1308 1272 43.68% 22.58% 33.74%+0.16 -4.8 0
2017-03-17 @ Jelgava W 2-150.1 1272 1308 33.74% 22.58% 43.68%-0.16 +4.8 3
2017-03-18 Spartaks Jurmala L 2-443.1 1186 1280 26.65% 21.69% 51.65%-0.60 -5.0 0
2017-03-18 @ Metta W 4-243.1 1280 1186 51.65% 21.69% 26.65%+0.60 +5.0 3
2017-03-18 Ventspils D 0-051.5 1293 1326 33.95% 22.59% 43.46%-0.25 +0.3 4
2017-03-18 @ Riga FC D 0-051.5 1326 1293 43.46% 22.59% 33.95%+0.25 -0.3 4
2017-04-01 Jelgava L 1-250.0 1326 1303 41.96% 22.67% 35.37%+0.09 -4.7 4
2017-04-01 @ Ventspils W 2-150.0 1303 1326 35.37% 22.67% 41.96%-0.09 +4.7 3
2017-04-02 FK RFS W 3-146.0 1181 1244 30.24% 22.26% 47.50%-0.42 +9.0 3
2017-04-02 @ Metta L 1-346.0 1244 1181 47.50% 22.26% 30.24%+0.42 -9.0 3
2017-04-02 Riga FC W 2-038.2 1285 1293 37.52% 22.72% 39.75%-0.09 +9.0 6
2017-04-02 @ Spartaks Jurmala L 0-238.2 1293 1285 39.75% 22.72% 37.52%+0.09 -9.0 4
2017-04-14 Spartaks Jurmala L 1-249.4 1308 1294 40.59% 22.71% 36.70%+0.03 -4.6 3
2017-04-14 @ Jelgava W 2-149.4 1294 1308 36.70% 22.71% 40.59%-0.03 +4.5 9
2017-04-15 FK RFS L 0-143.7 1277 1235 44.63% 22.52% 32.85%+0.20 -5.2 3
2017-04-15 @ Liepaja W 1-043.7 1235 1277 32.85% 22.52% 44.63%-0.20 +5.2 6
2017-04-16 Metta D 2-262.6 1284 1190 51.63% 21.70% 26.67%+0.50 -0.5 5
2017-04-16 @ Riga FC D 2-262.6 1190 1284 26.67% 21.70% 51.63%-0.50 +0.5 4
2017-04-21 Jelgava L 1-339.1 1190 1303 24.71% 21.27% 54.03%-0.71 -5.2 4
2017-04-21 @ Metta W 3-139.1 1303 1190 54.03% 21.27% 24.71%+0.71 +5.2 6
2017-04-22 Liepaja D 1-156.7 1322 1272 45.64% 22.44% 31.92%+0.24 -0.3 5
2017-04-22 @ Ventspils D 1-156.7 1272 1322 31.92% 22.44% 45.64%-0.24 +0.3 4
2017-04-22 Riga FC L 0-140.2 1240 1283 32.71% 22.51% 44.78%-0.30 -4.1 6
2017-04-22 @ FK RFS W 1-040.2 1283 1240 44.78% 22.51% 32.71%+0.30 +4.1 8
2017-04-29 Spartaks Jurmala L 1-342.9 1272 1299 34.97% 22.65% 42.39%-0.20 -7.0 4
2017-04-29 @ Liepaja W 3-142.9 1299 1272 42.39% 22.65% 34.97%+0.20 +7.0 12
2017-04-30 FK RFS W 1-038.6 1321 1236 50.45% 21.88% 27.67%+0.45 +3.5 8
2017-04-30 @ Ventspils L 0-138.6 1236 1321 27.67% 21.88% 50.45%-0.45 -3.5 6
2017-04-30 Riga FC L 0-336.8 1308 1287 41.61% 22.68% 35.71%+0.07 -13.5 6
2017-04-30 @ Jelgava W 3-036.8 1287 1308 35.71% 22.68% 41.61%-0.07 +13.5 11
2017-05-07 Liepaja L 1-340.2 1185 1265 28.22% 21.97% 49.81%-0.52 -5.9 4
2017-05-07 @ Metta W 3-140.2 1265 1185 49.81% 21.97% 28.22%+0.52 +5.9 7
2017-05-07 Ventspils D 0-051.5 1306 1325 35.94% 22.69% 41.38%-0.16 +0.2 13
2017-05-07 @ Spartaks Jurmala D 0-051.5 1325 1306 41.38% 22.69% 35.94%+0.16 -0.1 9
2017-05-08 Jelgava L 0-139.4 1232 1295 30.34% 22.27% 47.39%-0.41 -3.8 6
2017-05-08 @ FK RFS W 1-039.4 1295 1232 47.39% 22.27% 30.34%+0.41 +3.8 9
2017-05-12 FK RFS W 2-034.4 1306 1229 49.37% 22.03% 28.59%+0.40 +6.8 16
2017-05-12 @ Spartaks Jurmala L 0-234.4 1229 1306 28.59% 22.03% 49.37%-0.40 -6.9 6
2017-05-13 Liepaja L 0-239.1 1301 1271 42.89% 22.63% 34.49%+0.13 -9.5 11
2017-05-13 @ Riga FC W 2-039.1 1271 1301 34.49% 22.63% 42.89%-0.13 +9.5 10
2017-05-13 Ventspils D 1-156.9 1179 1325 21.61% 20.37% 58.02%-0.90 +1.0 5
2017-05-13 @ Metta D 1-156.9 1325 1179 58.02% 20.37% 21.61%+0.90 -0.9 10
2017-05-21 Jelgava W 3-036.5 1281 1299 36.10% 22.69% 41.21%-0.15 +13.4 13
2017-05-21 @ Liepaja L 0-336.5 1299 1281 41.21% 22.69% 36.10%+0.15 -13.4 9
2017-05-21 Metta W 1-036.5 1313 1180 56.46% 20.74% 22.80%+0.73 +3.0 19
2017-05-21 @ Spartaks Jurmala L 0-136.5 1180 1313 22.80% 20.74% 56.46%-0.73 -3.0 5
2017-05-21 Riga FC D 0-051.4 1324 1291 43.22% 22.61% 34.17%+0.14 -0.2 11
2017-05-21 @ Ventspils D 0-051.4 1291 1324 34.17% 22.61% 43.22%-0.14 +0.2 12
2017-05-27 Spartaks Jurmala D 1-156.7 1292 1316 35.30% 22.66% 42.04%-0.19 +0.2 13
2017-05-27 @ Riga FC D 1-156.7 1316 1292 42.04% 22.66% 35.30%+0.19 -0.2 20
2017-05-27 Ventspils W 1-044.0 1285 1323 33.39% 22.56% 44.06%-0.27 +5.1 12
2017-05-27 @ Jelgava L 0-144.0 1323 1285 44.06% 22.56% 33.39%+0.27 -5.2 11
2017-05-28 Metta W 3-251.1 1222 1177 44.98% 22.49% 32.53%+0.21 +3.6 9
2017-05-28 @ FK RFS L 2-351.1 1177 1222 32.53% 22.49% 44.98%-0.21 -3.6 5
2017-06-02 Jelgava D 1-156.7 1315 1290 42.20% 22.66% 35.14%+0.10 -0.2 21
2017-06-02 @ Spartaks Jurmala D 1-156.7 1290 1315 35.14% 22.66% 42.20%-0.10 +0.2 13
2017-06-02 Riga FC D 0-051.2 1173 1292 24.16% 21.13% 54.72%-0.74 +0.9 6
2017-06-02 @ Metta D 0-051.2 1292 1173 54.72% 21.13% 24.16%+0.74 -0.9 14
2017-06-03 Liepaja W 2-040.3 1225 1294 29.58% 22.18% 48.24%-0.45 +10.5 12
2017-06-03 @ FK RFS L 0-240.3 1294 1225 48.24% 22.18% 29.58%+0.45 -10.5 13
2017-06-17 FK RFS W 3-033.2 1291 1236 46.40% 22.37% 31.23%+0.27 +10.7 17
2017-06-17 @ Riga FC L 0-333.2 1236 1291 31.23% 22.37% 46.40%-0.27 -10.7 12
2017-06-17 Metta W 2-032.7 1291 1174 54.44% 21.18% 24.38%+0.63 +5.9 16
2017-06-17 @ Jelgava L 0-232.7 1174 1291 24.38% 21.18% 54.44%-0.63 -5.9 6
2017-06-18 Ventspils W 1-043.9 1284 1318 33.84% 22.59% 43.57%-0.25 +5.1 16
2017-06-18 @ Liepaja L 0-143.9 1318 1284 43.57% 22.59% 33.84%+0.25 -5.1 11
2017-06-22 Jelgava W 1-042.2 1302 1296 39.38% 22.73% 37.89%-0.02 +4.6 20
2017-06-22 @ Riga FC L 0-142.2 1296 1302 37.89% 22.73% 39.38%+0.02 -4.6 16
2017-06-22 Liepaja D 1-156.7 1315 1289 42.42% 22.65% 34.93%+0.11 -0.2 22
2017-06-22 @ Spartaks Jurmala D 1-156.7 1289 1315 34.93% 22.65% 42.42%-0.11 +0.2 17
2017-06-22 Ventspils W 4-250.7 1225 1313 27.36% 21.83% 50.81%-0.56 +8.5 15
2017-06-22 @ FK RFS L 2-450.7 1313 1225 50.81% 21.83% 27.36%+0.56 -8.5 11
2017-07-16 FK RFS D 1-156.4 1292 1234 46.84% 22.33% 30.83%+0.29 -0.4 17
2017-07-16 @ Jelgava D 1-156.4 1234 1292 30.83% 22.33% 46.84%-0.29 +0.4 16
2017-07-16 Metta L 0-146.2 1289 1168 54.97% 21.07% 23.96%+0.65 -6.2 17
2017-07-16 @ Liepaja W 1-046.2 1168 1289 23.96% 21.07% 54.97%-0.65 +6.2 9
2017-07-24 Metta W 3-030.1 1305 1175 56.16% 20.81% 23.03%+0.71 +8.2 14
2017-07-24 @ Ventspils L 0-330.1 1175 1305 23.03% 20.81% 56.16%-0.71 -8.2 9
2017-07-24 Riga FC W 2-149.7 1283 1306 35.32% 22.66% 42.01%-0.19 +4.7 20
2017-07-24 @ Liepaja L 1-249.7 1306 1283 42.01% 22.66% 35.32%+0.19 -4.7 20
2017-07-30 Spartaks Jurmala L 0-231.5 1166 1315 21.34% 20.27% 58.39%-0.92 -5.2 9
2017-07-30 @ Metta W 2-031.5 1315 1166 58.39% 20.27% 21.34%+0.92 +5.2 25
2017-07-30 Ventspils L 0-237.6 1302 1313 37.04% 22.72% 40.24%-0.11 -8.5 20
2017-07-30 @ Riga FC W 2-037.6 1313 1302 40.24% 22.72% 37.04%+0.11 +8.5 17
2017-07-31 Liepaja L 1-344.2 1292 1287 39.23% 22.73% 38.04%-0.02 -7.7 17
2017-07-31 @ Jelgava W 3-144.2 1287 1292 38.04% 22.73% 39.23%+0.02 +7.7 23
2017-08-05 Jelgava W 1-040.8 1321 1284 43.96% 22.56% 33.48%+0.17 +4.2 20
2017-08-05 @ Ventspils L 0-140.8 1284 1321 33.48% 22.56% 43.96%-0.17 -4.1 17
2017-08-05 Riga FC W 2-036.9 1320 1293 42.50% 22.64% 34.85%+0.11 +8.1 28
2017-08-05 @ Spartaks Jurmala L 0-236.9 1293 1320 34.85% 22.64% 42.50%-0.11 -8.1 20
2017-08-07 Metta W 1-038.6 1234 1161 48.81% 22.11% 29.08%+0.38 +3.7 19
2017-08-07 @ FK RFS L 0-138.6 1161 1234 29.08% 22.11% 48.81%-0.38 -3.7 9
2017-08-09 Spartaks Jurmala W 4-036.7 1325 1328 38.22% 22.73% 39.05%-0.07 +16.8 23
2017-08-09 @ Ventspils L 0-436.7 1328 1325 39.05% 22.73% 38.22%+0.07 -16.8 28
2017-08-12 FK RFS W 2-035.2 1295 1238 46.70% 22.34% 30.96%+0.29 +7.3 26
2017-08-12 @ Liepaja L 0-235.2 1238 1295 30.96% 22.34% 46.70%-0.29 -7.3 19
2017-08-13 Metta W 4-030.3 1285 1157 55.85% 20.88% 23.27%+0.70 +10.8 23
2017-08-13 @ Riga FC L 0-430.3 1157 1285 23.27% 20.88% 55.85%-0.70 -10.8 9
2017-08-13 Spartaks Jurmala L 1-247.3 1280 1312 34.22% 22.61% 43.17%-0.23 -4.0 17
2017-08-13 @ Jelgava W 2-147.3 1312 1280 43.17% 22.61% 34.22%+0.23 +4.0 31
2017-08-21 Jelgava D 0-051.2 1147 1276 23.13% 20.84% 56.02%-0.80 +0.9 10
2017-08-21 @ Metta D 0-051.2 1276 1147 56.02% 20.84% 23.13%+0.80 -1.0 18
2017-08-21 Liepaja D 0-051.7 1342 1302 44.29% 22.54% 33.16%+0.18 -0.3 24
2017-08-21 @ Ventspils D 0-051.7 1302 1342 33.16% 22.54% 44.29%-0.18 +0.3 27
2017-08-21 Riga FC D 1-156.4 1230 1296 29.96% 22.23% 47.81%-0.43 +0.4 20
2017-08-21 @ FK RFS D 1-156.4 1296 1230 47.81% 22.23% 29.96%+0.43 -0.5 24
2017-08-25 Riga FC L 1-247.7 1275 1295 35.73% 22.68% 41.59%-0.17 -4.1 18
2017-08-25 @ Jelgava W 2-147.7 1295 1275 41.59% 22.68% 35.73%+0.17 +4.1 27
2017-08-26 FK RFS L 1-252.7 1342 1231 53.79% 21.31% 24.90%+0.60 -5.7 24
2017-08-26 @ Ventspils W 2-152.7 1231 1342 24.90% 21.31% 53.79%-0.60 +5.7 23
2017-08-26 Spartaks Jurmala L 1-248.3 1303 1316 36.81% 22.71% 40.48%-0.12 -4.2 27
2017-08-26 @ Liepaja W 2-148.3 1316 1303 40.48% 22.71% 36.81%+0.12 +4.2 34
2017-09-09 Liepaja L 2-536.3 1148 1298 21.15% 20.21% 58.65%-0.93 -5.5 10
2017-09-09 @ Metta W 5-236.3 1298 1148 58.65% 20.21% 21.15%+0.93 +5.5 30
2017-09-09 Ventspils L 0-335.7 1320 1336 36.31% 22.70% 40.99%-0.14 -12.1 34
2017-09-09 @ Spartaks Jurmala W 3-035.7 1336 1320 40.99% 22.70% 36.31%+0.14 +12.1 27
2017-09-10 Jelgava W 3-036.9 1237 1271 33.93% 22.59% 43.48%-0.25 +14.0 26
2017-09-10 @ FK RFS L 0-336.9 1271 1237 43.48% 22.59% 33.93%+0.25 -14.0 18
2017-09-16 Liepaja W 2-149.0 1299 1304 38.00% 22.73% 39.27%-0.07 +4.4 30
2017-09-16 @ Riga FC L 1-249.0 1304 1299 39.27% 22.73% 38.00%+0.07 -4.4 30
2017-09-16 Spartaks Jurmala W 1-044.5 1251 1308 30.98% 22.34% 46.67%-0.38 +5.4 29
2017-09-16 @ FK RFS L 0-144.5 1308 1251 46.67% 22.34% 30.98%+0.38 -5.4 34
2017-09-17 Ventspils D 2-263.7 1142 1348 16.82% 18.31% 64.87%-1.25 +1.0 11
2017-09-17 @ Metta D 2-263.7 1348 1142 64.87% 18.31% 16.82%+1.25 -1.0 28
2017-09-24 Metta W 3-137.3 1302 1143 59.62% 19.94% 20.43%+0.88 +4.3 37
2017-09-24 @ Spartaks Jurmala L 1-337.3 1143 1302 20.43% 19.94% 59.62%-0.88 -4.3 11
2017-09-24 Riga FC L 0-240.2 1347 1304 44.79% 22.51% 32.71%+0.20 -9.8 28
2017-09-24 @ Ventspils W 2-040.2 1304 1347 32.71% 22.51% 44.79%-0.20 +9.8 33
2017-09-25 Jelgava L 0-143.9 1299 1257 44.71% 22.51% 32.78%+0.20 -5.2 30
2017-09-25 @ Liepaja W 1-043.9 1257 1299 32.78% 22.51% 44.71%-0.20 +5.2 21
2017-09-27 FK RFS L 1-346.0 1307 1256 45.78% 22.43% 31.79%+0.25 -8.7 37
2017-09-27 @ Spartaks Jurmala W 3-146.0 1256 1307 31.79% 22.43% 45.78%-0.25 +8.7 32
2017-09-30 FK RFS W 2-041.8 1139 1265 23.43% 20.93% 55.64%-0.78 +11.8 14
2017-09-30 @ Metta L 0-241.8 1265 1139 55.64% 20.93% 23.43%+0.78 -11.8 32
2017-09-30 Spartaks Jurmala L 0-143.2 1314 1298 40.91% 22.70% 36.38%+0.04 -4.9 33
2017-09-30 @ Riga FC W 1-043.2 1298 1314 36.38% 22.70% 40.91%-0.04 +4.9 40
2017-09-30 Ventspils L 0-139.2 1262 1337 28.76% 22.06% 49.18%-0.49 -3.6 21
2017-09-30 @ Jelgava W 1-039.2 1337 1262 49.18% 22.06% 28.76%+0.49 +3.7 31
2017-10-14 Jelgava L 0-144.0 1303 1258 44.94% 22.50% 32.56%+0.21 -5.2 40
2017-10-14 @ Spartaks Jurmala W 1-044.0 1258 1303 32.56% 22.50% 44.94%-0.21 +5.2 24
2017-10-14 Liepaja L 1-246.7 1253 1294 32.96% 22.53% 44.51%-0.29 -3.9 32
2017-10-14 @ FK RFS W 2-146.7 1294 1253 44.51% 22.53% 32.96%+0.29 +3.9 33
2017-10-14 Riga FC L 0-231.1 1151 1309 20.50% 19.97% 59.53%-0.97 -5.0 14
2017-10-14 @ Metta W 2-031.1 1309 1151 59.53% 19.97% 20.50%+0.97 +5.0 36
2017-10-22 FK RFS L 0-144.8 1314 1249 47.75% 22.23% 30.02%+0.33 -5.5 36
2017-10-22 @ Riga FC W 1-044.8 1249 1314 30.02% 22.23% 47.75%-0.33 +5.5 35
2017-10-22 Metta D 1-156.4 1263 1146 54.65% 21.14% 24.21%+0.64 -0.8 25
2017-10-22 @ Jelgava D 1-156.4 1146 1263 24.21% 21.14% 54.65%-0.64 +0.8 15
2017-10-22 Ventspils D 1-157.0 1298 1341 32.76% 22.51% 44.73%-0.30 +0.3 34
2017-10-22 @ Liepaja D 1-157.0 1341 1298 44.73% 22.51% 32.76%+0.30 -0.3 32
2017-10-28 Jelgava D 2-262.8 1308 1263 45.12% 22.48% 32.40%+0.22 -0.2 37
2017-10-28 @ Riga FC D 2-262.8 1263 1308 32.40% 22.48% 45.12%-0.22 +0.2 26
2017-10-28 Liepaja W 2-038.0 1297 1298 38.50% 22.73% 38.77%-0.05 +8.8 43
2017-10-28 @ Spartaks Jurmala L 0-238.0 1298 1297 38.77% 22.73% 38.50%+0.05 -8.8 34
2017-10-28 Ventspils L 2-444.0 1254 1341 27.54% 21.86% 50.60%-0.55 -5.1 35
2017-10-28 @ FK RFS W 4-244.0 1341 1254 50.60% 21.86% 27.54%+0.55 +5.1 35
2017-11-04 FK RFS W 1-041.4 1263 1249 40.58% 22.71% 36.71%+0.03 +4.5 29
2017-11-04 @ Jelgava L 0-141.4 1249 1263 36.71% 22.71% 40.58%-0.03 -4.5 35
2017-11-04 Metta W 1-036.0 1290 1146 57.76% 20.43% 21.81%+0.79 +2.8 37
2017-11-04 @ Liepaja L 0-136.0 1146 1290 21.81% 20.43% 57.76%-0.79 -2.8 15
2017-11-04 Spartaks Jurmala L 2-355.8 1346 1306 44.27% 22.54% 33.19%+0.18 -4.6 35
2017-11-04 @ Ventspils W 3-255.8 1306 1346 33.19% 22.54% 44.27%-0.18 +4.6 46

Biggest Upsets

The 25 games where the underdog won despite the lowest pregame win probability. Underdog Win % is the winner's pregame chance of winning the game (lower = bigger upset). @ before a team name indicates the away side. An asterisk (*) after the date marks a playoff game.

# Date Underdog Win % Winning Team Losing Team
Team Elo Score Team Elo Score
1 2017-09-30 23.43% @ Metta 1139 2 FK RFS 1265 0
2 2017-07-16 23.96% Metta 1168 1 @ Liepaja 1289 0
3 2017-08-26 24.90% FK RFS 1231 2 @ Ventspils 1342 1
4 2017-06-22 27.36% @ FK RFS 1225 4 Ventspils 1313 2
5 2017-06-03 29.58% @ FK RFS 1225 2 Liepaja 1294 0
6 2017-10-22 30.02% FK RFS 1249 1 @ Riga FC 1314 0
7 2017-04-02 30.24% @ Metta 1181 3 FK RFS 1244 1
8 2017-09-16 30.98% @ FK RFS 1251 1 Spartaks Jurmala 1308 0
9 2017-09-27 31.79% FK RFS 1256 3 @ Spartaks Jurmala 1307 1
10 2017-03-12 32.35% @ FK RFS 1239 2 Spartaks Jurmala 1285 1
11 2017-10-14 32.56% Jelgava 1258 1 @ Spartaks Jurmala 1303 0
12 2017-09-24 32.71% Riga FC 1304 2 @ Ventspils 1347 0
13 2017-09-25 32.78% Jelgava 1257 1 @ Liepaja 1299 0
14 2017-04-15 32.85% FK RFS 1235 1 @ Liepaja 1277 0
15 2017-11-04 33.19% Spartaks Jurmala 1306 3 @ Ventspils 1346 2
16 2017-05-27 33.39% @ Jelgava 1285 1 Ventspils 1323 0
17 2017-03-17 33.74% Liepaja 1272 2 @ Jelgava 1308 1
18 2017-06-18 33.84% @ Liepaja 1284 1 Ventspils 1318 0
19 2017-09-10 33.93% @ FK RFS 1237 3 Jelgava 1271 0
20 2017-05-13 34.49% Liepaja 1271 2 @ Riga FC 1301 0
21 2017-07-24 35.32% @ Liepaja 1283 2 Riga FC 1306 1
22 2017-04-01 35.37% Jelgava 1303 2 @ Ventspils 1326 1
23 2017-04-30 35.71% Riga FC 1287 3 @ Jelgava 1308 0
24 2017-05-21 36.10% @ Liepaja 1281 3 Jelgava 1299 0
25 2017-09-30 36.38% Spartaks Jurmala 1298 1 @ Riga FC 1314 0

Biggest Elo Changes

The 25 games that resulted in the largest shift in Elo rating.

# Date Elo Δ Winning Team Losing Team Tie %
Team Score Elo Win % Team Score Elo Win %
1 2017-08-09 16.77 @ Ventspils 4 1325 38.22% Spartaks Jurmala 0 1328 39.05% 22.73%
2 2017-09-10 13.98 @ FK RFS 3 1237 33.93% Jelgava 0 1271 43.48% 22.59%
3 2017-04-30 13.50 Riga FC 3 1287 35.71% @ Jelgava 0 1308 41.61% 22.68%
4 2017-05-21 13.38 @ Liepaja 3 1281 36.10% Jelgava 0 1299 41.21% 22.69%
5 2017-09-09 12.13 Ventspils 3 1336 40.99% @ Spartaks Jurmala 0 1320 36.31% 22.70%
6 2017-09-30 11.81 @ Metta 2 1139 23.43% FK RFS 0 1265 55.64% 20.93%
7 2017-08-13 10.79 @ Riga FC 4 1285 55.85% Metta 0 1157 23.27% 20.88%
8 2017-06-17 10.73 @ Riga FC 3 1291 46.40% FK RFS 0 1236 31.23% 22.37%
9 2017-06-03 10.47 @ FK RFS 2 1225 29.58% Liepaja 0 1294 48.24% 22.18%
10 2017-09-24 9.85 Riga FC 2 1304 32.71% @ Ventspils 0 1347 44.79% 22.51%
11 2017-05-13 9.52 Liepaja 2 1271 34.49% @ Riga FC 0 1301 42.89% 22.63%
12 2017-04-02 8.96 @ Spartaks Jurmala 2 1285 37.52% Riga FC 0 1293 39.75% 22.72%
13 2017-04-02 8.96 @ Metta 3 1181 30.24% FK RFS 1 1244 47.50% 22.26%
14 2017-10-28 8.78 @ Spartaks Jurmala 2 1297 38.50% Liepaja 0 1298 38.77% 22.73%
15 2017-09-27 8.69 FK RFS 3 1256 31.79% @ Spartaks Jurmala 1 1307 45.78% 22.43%
16 2017-07-30 8.48 Ventspils 2 1313 40.24% @ Riga FC 0 1302 37.04% 22.72%
17 2017-06-22 8.47 @ FK RFS 4 1225 27.36% Ventspils 2 1313 50.81% 21.83%
18 2017-07-24 8.18 @ Ventspils 3 1305 56.16% Metta 0 1175 23.03% 20.81%
19 2017-08-05 8.09 @ Spartaks Jurmala 2 1320 42.50% Riga FC 0 1293 34.85% 22.64%
20 2017-07-31 7.68 Liepaja 3 1287 38.04% @ Jelgava 1 1292 39.23% 22.73%
21 2017-08-12 7.33 @ Liepaja 2 1295 46.70% FK RFS 0 1238 30.96% 22.34%
22 2017-04-29 7.02 Spartaks Jurmala 3 1299 42.39% @ Liepaja 1 1272 34.97% 22.65%
23 2017-05-12 6.85 @ Spartaks Jurmala 2 1306 49.37% FK RFS 0 1229 28.59% 22.03%
24 2017-07-16 6.20 Metta 1 1168 23.96% @ Liepaja 0 1289 54.97% 21.07%
25 2017-06-17 5.94 @ Jelgava 2 1291 54.44% Metta 0 1174 24.38% 21.18%

Most & Least Exciting Games

The season’s games ranked by excitement rating (0–100) — a blend of closeness, upset, team quality, and scoring. An asterisk (*) after the date indicates a playoff game.

# Date Excitement Winning Team Losing Team Tie %
Team Score Elo Win % Team Score Elo Win %
1 2017-09-17 63.7 @ Metta 2 1142 16.82% Ventspils 2 1348 64.87% 18.31%
2 2017-10-28 62.8 @ Riga FC 2 1308 45.12% Jelgava 2 1263 32.40% 22.48%
3 2017-04-16 62.6 @ Riga FC 2 1284 51.63% Metta 2 1190 26.67% 21.70%
4 2017-10-22 57.0 @ Liepaja 1 1298 32.76% Ventspils 1 1341 44.73% 22.51%
5 2017-05-13 56.9 @ Metta 1 1179 21.61% Ventspils 1 1325 58.02% 20.37%
6 2017-04-22 56.7 @ Ventspils 1 1322 45.64% Liepaja 1 1272 31.92% 22.44%
7 2017-05-27 56.7 @ Riga FC 1 1292 35.30% Spartaks Jurmala 1 1316 42.04% 22.66%
8 2017-06-02 56.7 @ Spartaks Jurmala 1 1315 42.20% Jelgava 1 1290 35.14% 22.66%
9 2017-06-22 56.7 @ Spartaks Jurmala 1 1315 42.42% Liepaja 1 1289 34.93% 22.65%
10 2017-07-16 56.4 @ Jelgava 1 1292 46.84% FK RFS 1 1234 30.83% 22.33%
11 2017-08-21 56.4 @ FK RFS 1 1230 29.96% Riga FC 1 1296 47.81% 22.23%
12 2017-10-22 56.4 @ Jelgava 1 1263 54.65% Metta 1 1146 24.21% 21.14%
13 2017-11-04 55.8 Spartaks Jurmala 3 1306 33.19% @ Ventspils 2 1346 44.27% 22.54%
14 2017-08-26 52.7 FK RFS 2 1231 24.90% @ Ventspils 1 1342 53.79% 21.31%
15 2017-08-21 51.7 @ Ventspils 0 1342 44.29% Liepaja 0 1302 33.16% 22.54%
16 2017-03-18 51.5 @ Riga FC 0 1293 33.95% Ventspils 0 1326 43.46% 22.59%
17 2017-05-07 51.5 @ Spartaks Jurmala 0 1306 35.94% Ventspils 0 1325 41.38% 22.69%
18 2017-05-21 51.4 @ Ventspils 0 1324 43.22% Riga FC 0 1291 34.17% 22.61%
19 2017-06-02 51.2 @ Metta 0 1173 24.16% Riga FC 0 1292 54.72% 21.13%
20 2017-08-21 51.2 @ Metta 0 1147 23.13% Jelgava 0 1276 56.02% 20.84%
21 2017-05-28 51.1 @ FK RFS 3 1222 44.98% Metta 2 1177 32.53% 22.49%
22 2017-06-22 50.7 @ FK RFS 4 1225 27.36% Ventspils 2 1313 50.81% 21.83%
23 2017-03-12 50.2 @ FK RFS 2 1239 32.35% Spartaks Jurmala 1 1285 45.17% 22.48%
24 2017-03-17 50.1 Liepaja 2 1272 33.74% @ Jelgava 1 1308 43.68% 22.58%
25 2017-04-01 50.0 Jelgava 2 1303 35.37% @ Ventspils 1 1326 41.96% 22.67%
# Date Excitement Winning Team Losing Team Tie %
Team Score Elo Win % Team Score Elo Win %
1 2017-07-24 30.1 @ Ventspils 3 1305 56.16% Metta 0 1175 23.03% 20.81%
2 2017-08-13 30.3 @ Riga FC 4 1285 55.85% Metta 0 1157 23.27% 20.88%
3 2017-10-14 31.1 Riga FC 2 1309 59.53% @ Metta 0 1151 20.50% 19.97%
4 2017-07-30 31.5 Spartaks Jurmala 2 1315 58.39% @ Metta 0 1166 21.34% 20.27%
5 2017-03-11 32.3 @ Ventspils 2 1321 56.09% Metta 0 1191 23.08% 20.83%
6 2017-06-17 32.7 @ Jelgava 2 1291 54.44% Metta 0 1174 24.38% 21.18%
7 2017-06-17 33.2 @ Riga FC 3 1291 46.40% FK RFS 0 1236 31.23% 22.37%
8 2017-05-12 34.4 @ Spartaks Jurmala 2 1306 49.37% FK RFS 0 1229 28.59% 22.03%
9 2017-08-12 35.2 @ Liepaja 2 1295 46.70% FK RFS 0 1238 30.96% 22.34%
10 2017-09-09 35.7 Ventspils 3 1336 40.99% @ Spartaks Jurmala 0 1320 36.31% 22.70%
11 2017-11-04 36.0 @ Liepaja 1 1290 57.76% Metta 0 1146 21.81% 20.43%
12 2017-09-09 36.3 Liepaja 5 1298 58.65% @ Metta 2 1148 21.15% 20.21%
13 2017-05-21 36.5 @ Liepaja 3 1281 36.10% Jelgava 0 1299 41.21% 22.69%
14 2017-05-21 36.5 @ Spartaks Jurmala 1 1313 56.46% Metta 0 1180 22.80% 20.74%
15 2017-08-09 36.7 @ Ventspils 4 1325 38.22% Spartaks Jurmala 0 1328 39.05% 22.73%
16 2017-04-30 36.8 Riga FC 3 1287 35.71% @ Jelgava 0 1308 41.61% 22.68%
17 2017-08-05 36.9 @ Spartaks Jurmala 2 1320 42.50% Riga FC 0 1293 34.85% 22.64%
18 2017-09-10 36.9 @ FK RFS 3 1237 33.93% Jelgava 0 1271 43.48% 22.59%
19 2017-09-24 37.3 @ Spartaks Jurmala 3 1302 59.62% Metta 1 1143 20.43% 19.94%
20 2017-07-30 37.6 Ventspils 2 1313 40.24% @ Riga FC 0 1302 37.04% 22.72%
21 2017-10-28 38.0 @ Spartaks Jurmala 2 1297 38.50% Liepaja 0 1298 38.77% 22.73%
22 2017-04-02 38.2 @ Spartaks Jurmala 2 1285 37.52% Riga FC 0 1293 39.75% 22.72%
23 2017-04-30 38.6 @ Ventspils 1 1321 50.45% FK RFS 0 1236 27.67% 21.88%
24 2017-08-07 38.6 @ FK RFS 1 1234 48.81% Metta 0 1161 29.08% 22.11%
25 2017-04-21 39.1 Jelgava 3 1303 54.03% @ Metta 1 1190 24.71% 21.27%