Description
Full mathematical and statistical analysis of the 22nd round of the Swiss Super League, season 2025-2026: Prognostic model and risk management using the Harmony Index
Status and strategic context of the Swiss Super League in the 2025-2026 season
The Swiss Super League enters its most decisive phase with the arrival of the 22nd round of the 2025-2026 season. This period of the championship is traditionally considered a watershed, as teams approach the end of the regular season, after which the table is divided into a championship group and a survival group. The current campaign is characterized by unprecedented dynamics and unexpected shifts in the traditional hierarchy of power. While in the past clubs such as Young Boys and FC Basel have dominated undisputedly, the current season has brought FC Thun and FC Lugano to the forefront as the main contenders for the title.
The league’s statistical profile so far reveals a trend towards high scoring, with an average of 3.39 goals per game. This is a clear indicator of the offensive mindset of most head coaches, but it also highlights the serious deficiencies in the defensive organization of the teams in the lower half of the table. FC Thun occupy the leading position with 46 points after 21 matches played, demonstrating exceptional efficiency in both attack (46 goals scored) and defense (barely 25 conceded). At the other end of the spectrum, FC Winterthur are going through an extremely difficult period, occupying last place with only 10 points and a staggering 56 goals conceded, which is an average of 2.8 goals per game.
This report applies a rigorous mathematical protocol to analyze the six matches of the 22nd round. By using algorithms to calculate the strength of attack and defense, Poisson distributions and stability indices, it aims to remove the subjective factor and provide an objective assessment of probabilities. In a world where sports betting is often driven by emotions and biases, this model serves as a “ guardian angel” , focused on mathematical precision and long-term stability of predictions.
Calculation Methodology: Kara’s Mathematical Protocol
The analysis of each football match is carried out through the sequential application of an eight-step computational protocol. This process is designed to decipher raw statistics and translate them into predictive values for expected goals and likely outcomes.
Phase One and Two: Determining Offensive Power
The first step involves collecting basic data: percentage of wins ($W\%$), draws ($D\%$) and losses ($L\%$), as well as average number of goals scored and conceded. The attack strength for each team is calculated by summing the percentage of wins and losses with the average number of goals scored:
$$Attack\ Power = W\% + L\% + GF_{avg}$$
This formula takes into account not only the ability to convert, but also the team’s volatility – the fewer draws it has, the higher its attacking index.
Phase Three: Defining Defensive Stability
The strength of the defense is a reciprocal value that measures the resilience of the defensive wall against goals conceded and final results:
$$Defense\ Power = \ frac{ 1}{W\% – L\% + GA_{avg}}$$
The low index here (when combined with few goals conceded) actually means a higher statistical ” weight” of the defense in the final expected goals model ($xG$).
Fourth and fifth phases: xG and Poisson distribution
The expected goals for the home and away teams in a specific match are calculated as the arithmetic average between one team’s attack and the opponent’s defense:
$$xG_{Home} = \frac{Attack_{Home} + Defense_{Away }}{ 2}$$
$$xG_{Away} = \frac{Attack_{Away} + Defense_{Home }}{ 2}$$
The resulting $xG$ values are fed into a Poisson distribution to generate the probabilities for the three main outcomes: 1, X, and 2.
Sixth, seventh and eighth phases: Stability and Harmony index
To determine the risk, the model calculates two control indices:
- Stability (K): Uses the standard deviation of the probabilities to measure how ” spread out” they are about their mean. The formula is $(STDEV.P(1, X, 2) / AVERAGE( 1, X, 2)) \times 1.67$ with a ceiling of 0.99.
- Equality Index (L): Based on the absolute difference in attack/defense balance between the two teams: $ABS(ABS(Atk_{H} – Atk_{A}) – ABS(Def_{H} – Def_{A} ))$ .
The final Harmony Index (HI) combines these values:
$$HI = \frac{2}{K} + \ frac{ 1}{1 – L}$$
This index is the ultimate security filter. Values above 100 indicate a “Platinum Selection,” while those between 7.51 and 99.9 are considered medium risk.
Global statistical overview of the Super League before the 22nd round
Before proceeding to the detailed calculations for each match, it is necessary to analyze the current distribution of forces in the table. Data from the past 21 rounds (20 for some teams) provide the necessary basis for calculating the forces of attack and defense.
Table 1: Complete statistical database of the teams in the Super League (2025-2026)
| Team | M | P | P | H | VG | DG | P% | P% | 3% | Middle School | Sr. DG |
| FC Thun | 21 | 15 | 1 | 5 | 46 | 25 | 71.4% | 4.8% | 23.8% | 2.19 | 1.19 |
| FC Lugano | 21 | 12 | 3 | 6 | 37 | 26 | 57.1% | 14.3% | 28.6% | 1.76 | 1.24 |
| St. Gallen | 20 | 12 | 1 | 7 | 40 | 26 | 60.0% | 5.0% | 35.0% | 2.00 | 1.30 |
| FC Basel | 21 | 10 | 6 | 5 | 33 | 24 | 47.6% | 28.6% | 23.8% | 1.57 | 1.14 |
| FC Sion | 21 | 8 | 8 | 5 | 31 | 25 | 38.1% | 38.1% | 23.8% | 1.48 | 1.19 |
| Young Boys | 21 | 8 | 5 | 8 | 40 | 45 | 38.1% | 23.8% | 38.1% | 1.90 | 2.14 |
| Lausanne | 21 | 7 | 7 | 7 | 32 | 29 | 33.3% | 33.3% | 33.3% | 1.52 | 1.38 |
| FC Zurich | 21 | 7 | 4 | 10 | 32 | 40 | 33.3% | 19.0% | 47.6% | 1.52 | 1.90 |
| Servetus | 21 | 6 | 6 | 9 | 35 | 40 | 28.6% | 28.6% | 42.9% | 1.67 | 1.90 |
| FC Lucerne | 21 | 5 | 7 | 9 | 38 | 41 | 23.8% | 33.3% | 42.9% | 1.81 | 1.95 |
| Grasshoppers | 21 | 4 | 6 | 11 | 28 | 39 | 19.0% | 28.6% | 52.4% | 1.33 | 1.86 |
| Winterthur | 20 | 2 | 4 | 14 | 24 | 56 | 10.0% | 20.0% | 70.0% | 1.20 | 2.80 |
This statistical array reveals a serious polarization. FC Thun and St. Gallen have the most explosive attacks, while Winterthur’s defense is in a state of complete collapse. These extremes are essential when calculating the Harmony Index, as large differences in the attack/defense balance ($L$) can drastically increase or decrease the stability of the forecast.
Detailed analysis of match 1: Servette FC vs. FC Sion (31.01.2026)
The match between Servette and Sion pits two teams from the western part of Switzerland against each other, which always brings an extra charge. Servette is in ninth place, struggling to avoid the bottom four, while Sion is in fifth, but only four points behind fourth-placed Basel.
Step 1 and 2: Attack and Defense Strength
For Servet, the parameters are: $W = 28.6\%$, $L = 42.9\%$, $GF_{avg} = 1.67$.
$$Attack_{Servette} = 0.286 + 0.429 + 1.67 = 2.385$$
Their protection is calculated at $GA_{avg} = 1.90$:
$$Defense_{Servette} = \ frac{ 1}{0.286 – 0.429 + 1.90} = \frac{1}{1.757} = 0.569$$
For Zion, the parameters are: $W = 38.1\%$, $L = 23.8\%$, $GF_{avg} = 1.48$.
$$Attack_{Sion} = 0.381 + 0.238 + 1.48 = 2.099$$
Their defense at $GA_{avg} = 1.19$:
$$Defense_{Sion} = \ frac{ 1}{0.381 – 0.238 + 1.19} = \frac{1}{1.333} = 0.750$$
Step 3 and 4: xG and probabilities
Expected goals are:
$$xG_{Home} = \ frac{ 2.385 + 0.750}{2} = 1.568$$
$$xG_{Away} = \ frac{ 2.099 + 0.569}{2} = 1.334$$
Applying the Poisson distribution to these values generates the following probabilities:
- Home win (1): 40%
- Draw (X): 27%
- Away win (2): 33%
Step 5 to 8: Harmonies Index and Verdict V3
The difference in percentages is $V3 = 0.40 – 0.33 = 0.07$. According to the verdict algorithm, a value above 0.06 leads to a 1X prediction . Stability calculation (K): Mean = 0.333. Standard deviation $STDEV.P(0.40, 0.27, 0.33) = 0.053$.
$$K = \left(\frac{ 0.053}{ 0.333}\right) \times 1.67 = 0.266$$
Equality Index (L):
$$L = ABS( ABS(2.385 – 2.099) – ABS(0.569 – 0.750)) = ABS(0.286 – 0.181) = 0.105$$
Harmony index:
$$HI = \ frac {2}{0.266} + \frac{1}{1 – 0.105} = 7.519 + 1.117 = 8.636$$
This result puts the match in the medium risk category . Sion is the more balanced team, but Servette on their home field in Geneva demonstrates aggression, which the mathematical model considers as a factor for a partial advantage.
Detailed analysis of match 2: FC Winterthur vs Lausanne (31.01.2026)
This is a match between the weakest defense in the league and a team that maintains absolute balance in its performance. Winterthur has conceded 32 more goals than they have scored, which is a disastrous indicator for the elite level.
Attack and defense strength
Winterthur: $W = 10%$, $L = 70%$, $GF_{avg} = 1.20$, $GA_{avg} = 2.80$.
$$Attack_{Winterthur} = 0.10 + 0.70 + 1.20 = 2.00$$
$$Defense_{Winterthur} = \ frac{ 1}{0.10 – 0.70 + 2.80} = \frac{1}{2.20} = 0.455$$
Lausanne: $W = 33.3\%$, $L = 33.3\%$, $GF_{avg} = 1.52$, $GA_{avg} = 1.38$.
$$Attack_{Lausanne} = 0.333 + 0.333 + 1.52 = 2.186$$
$$Defense_{Lausanne} = \ frac{ 1}{0.333 – 0.333 + 1.38} = 0.725$$
xG, probabilities and Harmony index
$$xG_{Home} = \ frac{ 2.00 + 0.725}{2} = 1.363$$
$$xG_{Away} = \ frac{ 2.186 + 0.455}{2} = 1.321$$
The mathematical probability shows: 1: 34%, X: 27%, 2: 39%. However, the market odds for Lausanne are unusually low (1.75), suggesting external factors or an expected collapse for the home team. Using the market weights of 23% for 1 and 53% for 2: $V3 = 0.23 – 0.53 = -0.30$, leading to a verdict of 2 .
Stability (K): With a strong favorite, $K$ is usually higher. $STDEV.P(0.23, 0.24, 0.53) = 0.138$.
$$K = \left(\frac{ 0.138}{ 0.333}\right) \times 1.67 = 0.692 \\ L = ABS(ABS(2.00 – 2.186) – ABS(0.455 – 0.725)) = ABS(0.186 – 0.270) = 0.084 \\ HI = \frac{2}{0.692} + \frac{1}{1 – 0.084} = 2.89 + 1.09 = 3.98$$
The match is classified as high risk . The huge difference between the statistical xG model and market expectations creates an imbalance that makes the prediction uncertain, even though Lausanne is objectively the classier team.
Detailed analysis of match 3: Grasshoppers vs FC Lugano (31.01.2026)
” Grasshoppers” from Zurich are in a serious crisis, occupying 11th place, while Lugano is on a winning streak and breathing down the neck of the leader Thun. Lugano has one of the most stable defenses in the league, allowing an average of only 1.24 goals.
Force calculations
Grasshoppers: $W = 19\%$, $L = 52.4\%$, $GF_{avg} = 1.33$, $GA_{avg} = 1.86$.
$$Attack_{GCZ} = 0.19 + 0.524 + 1.33 = 2.044$$
$$Defense_{GCZ} = \ frac{ 1}{0.19 – 0.524 + 1.86} = \frac{1}{1.526} = 0.655$$
Lugano: $W = 57.1\%$, $L = 28.6\%$, $GF_{avg} = 1.76$, $GA_{avg} = 1.24$.
$$Attack_{Lugano} = 0.571 + 0.286 + 1.76 = 2.617$$
$$Defense_{Lugano} = \ frac{ 1}{0.571 – 0.286 + 1.24} = \frac{1}{1.525} = 0.656$$
Output analysis
$$xG_{Home} = \ frac{ 2.044 + 0.656}{2} = 1.350$$
$$xG_{Away} = \ frac{ 2.617 + 0.655}{2} = 1.636$$
Probabilities: 1: 30%, X: 26%, 2: 44%. $V3 = 0.30 – 0.44 = -0.14$. This result falls right in the verdict zone X2 . Harmony index:
$$K = \left(\frac{ 0.076}{ 0.333}\right) \times 1.67 = 0.381 \\ L = ABS(ABS(2.044 – 2.617) – ABS(0.655 – 0.656)) = ABS(0.573 – 0.001) = 0.572 \\ HI = \frac{2}{0.381} + \frac{1}{1 – 0.572} = 5.249 + 2.336 = 7.585$$
The match is in the medium risk zone . Lugano is the favorite, but Grasshoppers often manage to squeeze out draws at home through defensive play, which is reflected in the double chance of the prediction.
Detailed analysis of match 4: FC Luzern vs. St. Gallen (01.02.2026)
This is one of the most difficult matches to predict due to the high offensive activity of both teams. St. Gallen is in third place with 12 wins, while Luzern is in tenth, but with a whopping 38 goals scored.
Attack and defense strength
Alfalfa: $W = 23.8\%$, $L = 42.9\%$, $GF_{avg} = 1.81$, $GA_{avg} = 1.95$.
$$Attack_{Luzern} = 0.238 + 0.429 + 1.81 = 2.477$$
$$Defense_{Luzern} = \ frac{ 1}{0.238 – 0.429 + 1.95} = \frac{1}{1.759} = 0.569$$
St. Gallen: $W = 60\%$, $L = 35\%$, $GF_{avg} = 2.00$, $GA_{avg} = 1.30$.
$$Attack_{StGallen} = 0.60 + 0.35 + 2.00 = 2.950$$
$$Defense_{StGallen} = \ frac{ 1}{0.60 – 0.35 + 1.30} = \frac{1}{1.55} = 0.645$$
xG and Harmony Index
$$xG_{Home} = \ frac{ 2.477 + 0.645}{2} = 1.561$$
$$xG_{Away} = \ frac{ 2.950 + 0.569}{2} = 1.760$$
Probabilities: 1: 34%, X: 24%, 2: 42%. $V3 = 0.34 – 0.42 = -0.08$, which corresponds to verdict X2 (exactly on the border between X and X2).
$$K = \left(\frac{ 0.074}{ 0.333}\right) \times 1.67 = 0.371 \\ L = ABS(ABS(2.477 – 2.950) – ABS(0.569 – 0.645)) = ABS(0.473 – 0.076) = 0.397 \\ HI = \frac{2}{0.371} + \frac{1}{1 – 0.397} = 5.391 + 1.658 = 7.049$$
Category: High Risk . Although St. Gallen is higher in the standings, Luzern has the attacking capacity to neutralize the away team’s defense. This match promises goals, but the final outcome is statistically unstable.
Detailed analysis of match 5: FC Basel vs FC Thun (01.02.2026)
This is the derby of the round. Traditional giants Basel host temporary leader Thun. Basel have stabilized their performance, conceding only 24 goals, while Thun is flying on the wings of Christopher Ibayi (9 goals) and one of the most effective attacks in Europe this season.
Force calculations
Basel: $W = 47.6\%$, $L = 23.8\%$, $GF_{avg} = 1.57$, $GA_{avg} = 1.14$.
$$Attack_{Basel} = 0.476 + 0.238 + 1.57 = 2.284$$
$$Defense_{Basel} = \ frac{ 1}{0.476 – 0.238 + 1.14} = \frac{1}{1.378} = 0.726$$
Tun: $W = 71.4\%$, $L = 23.8\%$, $GF_{avg} = 2.19$, $GA_{avg} = 1.19$.
$$Attack_{Thun} = 0.714 + 0.238 + 2.19 = 3.142$$
$$Defense_{Thun} = \ frac{ 1}{0.714 – 0.238 + 1.19} = \frac{1}{1.666} = 0.600$$
Probabilities and Harmony Index
$$xG_{Home} = \ frac{ 2.284 + 0.600}{2} = 1.442$$
$$xG_{Away} = \ frac{ 3.142 + 0.726}{2} = 1.934$$
Probabilities: 1: 30%, X: 23%, 2: 47%. $V3 = 0.30 – 0.47 = -0.17$, which puts the verdict right on the border for 2 .
$$K = \left(\frac{ 0.103}{ 0.333}\right) \times 1.67 = 0.516 \\ L = ABS(ABS(2.284 – 3.142) – ABS(0.726 – 0.600)) = ABS(0.858 – 0.126) = 0.732 \\ HI = \frac{2}{0.516} + \frac{1}{1 – 0.732} = 3.876 + 3.731 = 7.607$$
Category: Medium risk . Thun is in exceptional form, but Basel at St. Jakob Park is always a dangerous opponent. The mathematical model slightly favors the visitors, but the high value of $L$ (drawability index) indicates that the match could develop in the direction of a clear victory for one of the two sides.
Detailed analysis of match 6: Young Boys vs FC Zurich (01.02.2026)
A clash between two teams with great history, but who are going through a poor season. Young Boys are sixth with 45 goals conceded, while Zurich are eighth with 10 losses.
Attack and defense strength
Young Boys: $W = 38.1\%$, $L = 38.1\%$, $GF_{avg} = 1.90$, $GA_{avg} = 2.14$.
$$Attack_{YB} = 0.381 + 0.381 + 1.90 = 2.662$$
$$Defense_{YB} = \ frac{ 1}{0.381 – 0.381 + 2.14} = 0.467$$
Zurich: $W = 33.3\%$, $L = 47.6\%$, $GF_{avg} = 1.52$, $GA_{avg} = 1.90$.
$$Attack_{Zurich} = 0.333 + 0.476 + 1.52 = 2.329$$
$$Defense_{Zurich} = \ frac{ 1}{0.333 – 0.476 + 1.90} = \frac{1}{1.757} = 0.569$$
Output analysis
$$xG_{Home} = \ frac{ 2.662 + 0.569}{2} = 1.615$$
$$xG_{Away} = \ frac{ 2.329 + 0.467}{2} = 1.398$$
Probabilities: 1: 42%, X: 24%, 2: 34%. $V3 = 0.42 – 0.34 = 0.08$. Verdict: 1X (in the range 0.06 – 0.1). Harmony Index:
$$K = \left(\frac{ 0.074}{ 0.333}\right) \times 1.67 = 0.371 \\ L = ABS(ABS(2.662 – 2.329) – ABS(0.467 – 0.569)) = ABS(0.333 – 0.102) = 0.231 \\ HI = \frac{2}{0.371} + \frac{1}{1 – 0.231} = 5.391 + 1.300 = 6.691$$
Category: High Risk . Both teams are extremely hesitant in defense, which makes any result possible. Young Boys have a slight advantage due to the home advantage and their stronger attack, but the statistical stability is low.
Second and third levels of insight: What does the data tell us?
Analysis of the 22nd round through the Harmony Index reveals several deep trends that remain hidden from a simple reading of the rankings.
First, there is a complete lack of “Platinum Selection” in this round. This is an indication of parity in the league and the fact that the favorites (such as Thun and Lugano) are facing opponents who have specific offensive tools to counter. The mathematical stability ($K$) is relatively low in almost all matches, meaning that the probabilities of the three outcomes are distributed relatively evenly. This reflects the “ open” nature of Swiss football, where even tail-ender Winterthur maintains offensive activity despite its defensive collapse.
Secondly, the draw index ($L$) is highest in the Basel – Thun match ($0.732$). This suggests a serious tactical imbalance that will likely result in a win for one of the two teams, rather than a draw. In a betting context, this makes the draw markets in this match extremely risky, despite the tempting odds.
Third, there is an interesting correlation between defensive strength and the Harmony Index. Teams with extremely weak defenses (Young Boys, Winterthur, Luzern) generate a lower HI, as their defensive unpredictability introduces a lot of “ noise” into the Poisson model. Conversely, the matches of Sion and Basel show higher mathematical robustness due to their more structured play.
Global summary table of the 22nd round
This table represents the final product of the mathematical analysis, categorized according to the new risk and verdict requirements.
| Meeting | xG (Home – Away) | Forecast | Verdict V3 | Harmonies Index | Category | Coefficient |
| Servette – Sion | 1.57 – 1.33 | 1X | 0.07 | 8.64 | Medium risk | 1.24 |
| Winterthur – Lausanne | 1.36 – 1.32 | 2 | -0.30 | 3.98 | High risk | 1.75 |
| Grasshoppers – Lugano | 1.35 – 1.64 | X2 | -0.14 | 7.59 | Medium risk | 1.23 |
| Lucerne – St. Gallen | 1.56 – 1.76 | X2 | -0.08 | 7.05 | High risk | 1.54 |
| Basel – Thun | 1.44 – 1.93 | 2 | -0.17 | 7.61 | Medium risk | 2.20 |
| Young Boys – Zurich | 1.62 – 1.40 | 1X | 0.08 | 6.69 | High risk | 1.22 |
Strategic insights and bankroll management
Based on the presented data, the professional approach to the 22nd round requires increased caution. The fact that no match crosses the 100-point mark for the Harmony Index means that there are no ” safe” bets in this round. The closest to the safety zone are the matches Servette – Sion and Basel – Thun, where mathematical logic and market trends converge the most.
For matches in the “High Risk” category ( Winterthur – Lausanne, Lucerne – St. Gallen, Young Boys – Zurich) it is recommended to avoid single bets with a large amount. These matches are more suitable for strategies based on ” live” betting , where you can observe the real state of the teams in the first 15-20 minutes before making a final decision. Particular attention should be paid to Winterthur, whose defense is so unstable that any early goal against them can lead to another crushing result.
In conclusion, the 22nd round of the Super League is an illustration of the balance between mathematical probability and the chaos of the game of football. Using ” Kara” as your mathematical advisor, you have an objective shield against the emotional traps of the market. Discipline and adherence to protocol are the only path to long-term success in this complex analytical landscape
