IHL stats & predictions

Hockey Club Bolzano

Hockey Club Bolzano has been in formidable form this season, boasting a robust defense and a dynamic offense. Their key player, Luca Ansoldi, has been instrumental in their recent successes. Bolzano's ability to control the pace of the game makes them a formidable opponent.

Asiago Hockey Club

Asiago HC, known for their resilient spirit and strategic gameplay, has consistently been one of the top contenders in the league. With players like Daniele Franzutti leading the charge, Asiago remains a team to watch out for.

S.G. Cortina

S.G. Cortina's aggressive play style and fast-paced offense have made them a fan favorite. Their ability to adapt quickly during matches gives them an edge over many competitors.

Hockey Club Milano Rossoblu

HC Milano Rossoblu's strength lies in their solid defense and disciplined play. They have been known to frustrate opponents with their strategic positioning and timely counterattacks.

Hockey Club Val Pusteria

Val Pusteria's recent performances have shown significant improvement, with their young squad displaying great potential. Their focus on teamwork and cohesion has paid off in recent matches.

Alleghe Hockey

Alleghe Hockey has maintained a steady presence in the league standings through consistent performances. Their balanced approach between offense and defense makes them a tough team to beat.

The upcoming matches feature several standout players who could make a significant impact on the game's outcome. Here are some players to keep an eye on:

• Luka Ansoldi (HC Bolzano): A versatile forward known for his scoring ability and playmaking skills, Ansoldi is expected to be pivotal in Bolzano's strategy against Asiago.
• Daniele Franzutti (Asiago HC): Franzutti's leadership on the ice and knack for clutch plays make him a key player for Asiago as they face Bolzano.
• Mattia Dal Corso (SG Cortina): Known for his speed and agility, Dal Corso is likely to be a major threat against HC Milano Rossoblu.
• Filippo Rota (HC Milano Rossoblu): As a defensive stalwart, Rota's ability to neutralize opposing offenses will be crucial in Cortina's matchup.
• Nicolas Fontanive (HC Val Pusteria): Fontanive's offensive prowess and leadership qualities are expected to shine as Val Pusteria takes on Alleghe Hockey.
• Federico Bernard (Alleghe Hockey): Bernard's experience and tactical awareness make him a vital component of Alleghe's game plan against Val Pusteria.

Betting enthusiasts have been closely analyzing the upcoming IHL matches, providing expert predictions based on team performances, player form, and historical data. Here are some insights into what the experts are saying:

• Hockey Club Bolzano vs. Asiago HC: Experts predict a close match with Bolzano having a slight edge due to their home advantage and recent form. The over/under total goals prediction stands at around 5.5.
• S.G. Cortina vs. HC Milano Rossoblu: Given Cortina's aggressive style and Rossoblu's defensive strength, experts suggest betting on fewer than 6 goals scored in total.
• Hockey Club Val Pusteria vs. Alleghe Hockey: This match is expected to be tightly contested, with experts leaning towards a draw or a narrow victory for either team. The total goals prediction is set at around 4.5.

In addition to these specific predictions, experts recommend considering factors such as player injuries, weather conditions (for outdoor rinks), and recent team dynamics when placing bets.

Tips for Bettors

• Analyze Team Form: Look at the recent performances of both teams to gauge their current form and momentum.
• Consider Head-to-Head Records: Historical matchups can provide valuable insights into how teams might perform against each other.
• Monitor Player News: Stay updated on any last-minute injuries or lineup changes that could affect the game's outcome.
• Diversify Bets: Spread your bets across different outcomes to manage risk effectively.
• Bet Responsibly: Always gamble responsibly and within your means.

Tomorrow's matches are not just about scoring goals; they also offer fascinating tactical battles that could determine the outcome of each game. Here are some key tactical aspects to watch out for:

• Puck Possession: Teams that maintain puck possession tend to control the game better. Watch how teams like HC Bolzano manage their puck time against Asiago HC.
• Zonal Defense vs. Man-to-Man Marking: SG Cortina might employ zonal defense against HC Milano Rossoblu's man-to-man marking strategy, creating interesting clashes on the ice.
• Power Play Opportunities: Special teams' performance can be decisive in tight games. Keep an eye on how teams capitalize on power plays during critical moments.
• Comeback Strategies: Teams trailing by multiple goals often switch tactics mid-game. Observe how teams like HC Val Pusteria adjust their strategies when facing Alleghe Hockey.
• Glass Management: How teams handle physical play along the boards can impact their overall performance. Players' ability to win battles along the glass will be crucial in maintaining momentum.

Tactical nuances often make or break games in professional ice hockey, adding another layer of excitement for fans and analysts alike.

Injuries can significantly impact team performance, making injury reports crucial for fans and bettors alike. Here are some key injury updates ahead of tomorrow's matches:

• Hockey Club Bolzano: Luca Ansoldi is listed as day-to-day with a minor lower-body injury but is expected to play against Asiago HC unless there are any setbacks during warm-ups.
• S.G. Cortina: Mattia Dal Corso recently recovered from an ankle sprain and is back in full training but will be closely monitored during tomorrow's match against HC Milano Rossoblu.
• Hockey Club Milano Rossoblu: No major injuries reported; however, coach Marco De Biasio mentioned that Filippo Rota will need extra minutes in practice today due to fatigue from international duties earlier this week.
• Hockey Club Val Pusteria: Nicolas Fontanive missed last week’s game due to illness but has returned fit and ready for tomorrow’s clash with Alleghe Hockey.
• Alleghe Hockey: Federico Bernard remains sidelined with an upper-body injury sustained last month; his absence will be felt as he is one of Alleghe’s top defenders.davidzhong/CS5590<|file_sep|>/src/python/CS5590.py import numpy as np import scipy as sp from scipy import stats from scipy.stats import norm import pandas as pd import matplotlib.pyplot as plt class CS5590: """ We use R_j^2 = var(Y | X=j) / var(Y) where j = {1,...K} """ def __init__(self): pass def read_csv(self,file_name): self.df = pd.read_csv(file_name) def describe(self): print(self.df.describe()) def histogram(self): for column in self.df.columns: plt.figure(column) self.df[column].hist(bins=20) plt.show() def pairwise_scatter_plot(self): pd.plotting.scatter_matrix(self.df) plt.show() def linear_regression(self,x,y): self.x = self.df[x] self.y = self.df[y] self.n = self.x.shape[0] self.X = np.ones((self.n,self.x.shape[1]+1)) self.X[:,1:] = self.x.values self.B = np.dot(np.dot(np.linalg.inv(np.dot(self.X.T,self.X)),self.X.T),self.y.values) self.residuals = self.y.values - np.dot(self.X,self.B) self.variance = np.sum(np.power(self.residuals,self.n)) / (self.n - self.x.shape[1] -1) self.t_values = self.B / np.sqrt(np.diag(np.dot(np.dot(np.linalg.inv(np.dot(self.X.T,self.X)),self.variance),np.linalg.inv(np.dot(self.X.T,self.X)))).reshape(-1,)) print('Betas: n',self.B) print('Variance: n',self.variance) print('t-values: n',self.t_values) def logistic_regression(self,x,y): self.x = self.df[x] self.y = self.df[y] self.n = self.x.shape[0] self.p = self.x.shape[1] self.X = np.ones((self.n,self.p+1)) self.X[:,1:] = self.x.values self.theta_0 = np.zeros((self.p+1,)) for i in range(100): sigmoid_val = sp.special.expit(np.dot(self.X,self.theta_0)) error_vec = self.y.values - sigmoid_val update_vec = np.dot(error_vec,self.X) / sigmoid_val * (1 - sigmoid_val) self.theta_0 += update_vec.mean(axis=0) if np.linalg.norm(update_vec.mean(axis=0)) <= .00001: break def k_means_clustering(self,x,k,max_iter=100): pass def silhouette_coefficient(self,x,k,max_iter=100): pass def linear_discriminant_analysis(self,x,y): # Find means of each class class_labels = y.unique() class_means = [] # Find class variances class_vars = [] # Find overall mean overall_mean = x.mean() # Find overall variance overall_var = x.var() # Find covariance matrix cov_matrix=np.cov(x.T) # Calculate means per class for label in class_labels: class_means.append(x[y == label].mean()) # Calculate variance per class # Assume variance across classes is constant so take mean # Variance across classes should be equal under LDA assumption if __name__ == '__main__': cs5590=CS5590() cs5590.read_csv('ex2data2.txt') cs5590.describe() cs5590.histogram() cs5590.pairwise_scatter_plot() cs5590.linear_regression(['X1','X2'],'Y')<|repo_name|>davidzhong/CS5590<|file_sep|>/src/python/exercise.py import numpy as np import scipy as sp from scipy import stats from scipy.stats import norm import pandas as pd import matplotlib.pyplot as plt class exercise: def __init__(self): pass def read_csv(self,file_name): self.dataframe=pd.read_csv(file_name) def describe(self): print(self.dataframe.describe()) def histogram(self): for column in self.dataframe.columns: plt.figure(column) self.dataframe[column].hist(bins=20) plt.show() def pairwise_scatter_plot(self): pd.plotting.scatter_matrix(self.dataframe) plt.show() def linear_regression(self,x,y): """ Ordinary Least Squares Linear Regression """ # Define x & y self.x=self.dataframe[x] self.y=self.dataframe[y] # Number of observations & number of features n=self.x.shape[0] p=self.x.shape[1] # Add intercept X=np.ones((n,p+1)) X[:,1:]=self.x.values # Compute B using Normal Equation B=np.dot(np.dot(np.linalg.inv(np.dot(X.T,X)),X.T),self.y.values) # Compute residuals & variance estimator residuals=self.y.values-np.dot(X,B) variance=np.sum(np.power(residuals,n)) / (n-p-1) # Compute t-values t_values=B / np.sqrt(np.diag(np.dot(np.dot(np.linalg.inv(np.dot(X.T,X)),variance),np.linalg.inv(np.dot(X.T,X)))).reshape(-1)) print('Betas: n',B) print('Variance: n',variance) print('t-values: n',t_values) def logistic_regression(self,x,y): """ Logistic Regression using Newton-Raphson method """ # Define x & y self.x=self.dataframe[x] self.y=self.dataframe[y] # Number of observations & number of features n=self.x.shape[0] p=self.x.shape[1] # Add intercept X=np.ones((n,p+1)) X[:,1:]=self.x.values # Initialize theta_0 theta_0=np.zeros((p+1,),) # Newton-Raphson method for i in range(100): sigmoid_val=sp.special.expit(np.dot(X,theta_0)) error_vec=self.y.values-sigmoid_val update_vec=np.dot(error_vec,X) / sigmoid_val * (1-sigmoid_val) theta_0+=update_vec.mean(axis=0) if np.linalg.norm(update_vec.mean(axis=0))<=.00001: break if __name__=='__main__': exercise=read_csv('ex2data2.txt') exercise.describe() exercise.histogram() exercise.pairwise_scatter_plot() exercise.linear_regression(['X1','X2'],'Y') exercise.logistic_regression(['X1','X2'],'Y')<|repo_name|>davidzhong/CS5590<|file_sep|>/src/python/exercise_linear_discriminant.py import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.preprocessing import LabelEncoder from sklearn.discriminant_analysis import LinearDiscriminantAnalysis def main(): data=pd.read_csv("wine.csv") data.head() X=data.iloc[:,1:].values y=data.iloc[:,0].values label_encoder=LabelEncoder() y=label_encoder.fit_transform(y) clf=LinearDiscriminantAnalysis() clf.fit(X,y) y_pred=clf.predict(X) print("Number mislabeled points out of a total %d points : %d" % (X.shape[0],(y != y_pred).sum())) if __name__=="__main__": main()<|repo_name|>davidzhong/CS5590<|file_sep|>/src/python/exercise_k_means.py import numpy as np import pandas as pd from sklearn.cluster import KMeans from sklearn.metrics import silhouette_score def main(): data=pd.read_csv("iris.csv") X=data.iloc[:, :-1].values y=data.iloc[:, -1].values clf=KMeans(n_clusters=3) clf.fit(X) y_pred=clf.predict(X) print("Number mislabeled points out of a total %d points : %d" % (X.shape[0],(y != y_pred).sum())) silhouette_avg=silhouette_score(X,y_pred) print("For n_clusters =",3,"The average silhouette_score is :",silhouette_avg) if __name__=="__main__": main()<|file_sep|># CS5590 A repository containing code written during CS559 Machine Learning Class at University of Houston. ### Exercise