SHL stats & predictions

Exploring Tomorrow's SHL Sweden Ice-Hockey Matches: Expert Predictions and Betting Insights

As a local South African with a passion for ice-hockey, I'm excited to dive into the upcoming SHL Sweden matches scheduled for tomorrow. The Swedish Hockey League (SHL) is renowned for its thrilling gameplay and fierce competition, making it a must-watch for enthusiasts around the globe. In this comprehensive guide, we'll explore the key matchups, analyze team performances, and provide expert betting predictions to help you make informed decisions.

Matchup Overview

• Frölunda HC vs. HV71
• Rögle BK vs. Linköpings HC
• Färjestad BK vs. Djurgårdens IF
• Växjö Lakers vs. Brynäs IF

Each of these games promises to be a spectacle, with top-tier teams battling it out on the ice. Let's delve deeper into each matchup, examining the strengths and weaknesses of the teams involved.

Frölunda HC vs. HV71: A Clash of Titans

Frölunda HC, known for their strategic gameplay and robust defense, are set to face HV71, a team that has shown remarkable resilience this season. Frölunda's star player, William Nylander, is expected to lead the charge with his exceptional skills and scoring ability.

• Frölunda HC Strengths:
  • Strong defensive lineup
  • Superior puck control
  • Highly skilled forwards
• HV71 Strengths:
  • Aggressive offense
  • Experienced coaching staff
  • Dynamic power play strategies

Betting Tip: Frölunda HC is favored to win, but keep an eye on HV71's power play opportunities.

Rögle BK vs. Linköpings HC: A Battle of Wits

Rögle BK has been performing exceptionally well this season, thanks to their disciplined play and tactical acumen. They will be up against Linköpings HC, a team known for their fast-paced style and quick transitions.

• Rögle BK Strengths:
  • Tactical discipline
  • Consistent goaltending
  • Strong penalty kill unit
• Linköpings HC Strengths:
  • Speed and agility
  • Potent offensive line
  • Effective zone entries

Betting Tip: Consider backing Rögle BK to win in regulation time, given their defensive prowess.

Färjestad BK vs. Djurgårdens IF: An Exciting Encounter

Färjestad BK is one of the most storied clubs in SHL history, boasting a rich tradition and a dedicated fanbase. They will face Djurgårdens IF, a team that has been steadily climbing the ranks with their youthful energy and innovative strategies.

• Färjestad BK Strengths:
  • Experienced roster
  • Strong leadership on and off the ice
  • Effective forechecking system
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  • Djurgårdens IF Strengths: 
    • Youthful exuberance 
    • Innovative playmaking 
    • Dynamic special teams amitksr/Learning<|file_sep|>/DataScienceFromScratch/Chapter_8/8_1.py import matplotlib.pyplot as plt import numpy as np from sklearn.datasets import load_iris from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score iris = load_iris() X = iris["data"][:,(2,3)] # petal length and width y = (iris["target"]==2).astype(np.int) X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2) log_reg = LogisticRegression() log_reg.fit(X_train,y_train) def plot_funtion(X,y,kwargs): x0,x1 = X[:,0],X[:,1] #training points plt.scatter(x0,x1,c=y,cmap=plt.cm.Paired,s=15) #plot decision boundary x0_1,x0_2 = np.meshgrid( np.linspace(0,X[:,0].max(),50), np.linspace(0,X[:,1].max(),50) ) X_new = np.c_[x0_1.ravel(),x0_2.ravel()] y_predict = log_reg.predict(X_new).reshape(x0_1.shape) custom_cmap = plt.cm.Paired custom_cmap.set_over('k') plt.contourf(x0_1,x0_2,y_predict,alpha=0.2,cmap=custom_cmap) plot_funtion(X_train,y_train) plt.show() y_predict = log_reg.predict(X_test) accuracy_score(y_test,y_predict)<|repo_name|>amitksr/Learning<|file_sep|>/DataScienceFromScratch/Chapter_8/8_5.py import numpy as np def softmax(a): exp_a = np.exp(a) if len(a.shape)==1: return exp_a / exp_a.sum() else: return exp_a / exp_a.sum(axis=1).reshape(-1,1) softmax(np.array([1001,-1001])) def cross_entropy_error(y,t): if y.ndim == 1: t = t.reshape(1,t.size) y = y.reshape(1,y.size) batch_size = y.shape[0] return -np.sum(t*np.log(y+1e-7))/batch_size t=np.array([0,0,1]) y=np.array([.2,.2,.6]) cross_entropy_error(y,t) def softmax_loss(x,t): if x.ndim==1: x=x.reshape(1,x.size) t=t.reshape(1,t.size) batch_size=x.shape[0] return cross_entropy_error(softmax(x),t)<|repo_name|>amitksr/Learning<|file_sep|>/DataScienceFromScratch/Chapter_8/8_4.py import numpy as np a=np.random.randn(100) #mean calculation using loops sum=0 for i in a: sum+=i print(sum/len(a)) #mean calculation using numpy functions np.mean(a) #standard deviation calculation using loops sum=0 for i in a: sum+=(i-np.mean(a))2 print(np.sqrt(sum/(len(a)-1))) #standard deviation calculation using numpy functions np.std(a)<|repo_name|>amitksr/Learning<|file_sep|>/DataScienceFromScratch/Chapter_7/7_5.py import math def entropy(p): if p==0 or p==1: return 0 return - p*math.log(p,2) - (1-p)*math.log((1-p),2) def partition_entropy(subsets): total_count = sum([len(subset) for subset in subsets]) return sum([len(subset)/total_count * entropy(count_positives(subset)/len(subset)) for subset in subsets]) def count_positives(subset): return len([label for _,label in subset if label==+1]) def partition_classes(rows,column): t={} for row in rows: value=row[column] if value not in t: t[value]=[] t[value].append(row) return list(t.values()) #test data dataset=[[20,"M",10,"Y"], [30,"F",20,"N"], [40,"M",10,"Y"], [50,"M",20,"N"], [60,"M",30,"Y"], [70,"F",20,"Y"]] print(partition_entropy(partition_classes(dataset,-1)))<|file_sep|># -*- coding: utf-8 -*- """ Created on Sat Oct 13 15:03:58 2018 @author: amitsinghrawat """ import matplotlib.pyplot as plt import numpy as np class NeuralNetwork(): # ============================================================================= # def __init__(self,n_input,n_hidden,n_output): # # self.n_input=n_input #number of input nodes # self.n_hidden=n_hidden #number of hidden nodes # self.n_output=n_output #number of output nodes # # self.wih=np.random.rand(n_hidden,n_input) - 0.5 #weights between input and hidden layer; initialized randomly between -0.5 and +0.5; n_hidden rows; n_input columns; each column represents weights connected to one input node # self.who=np.random.rand(n_output,n_hidden) - 0.5 #weights between hidden and output layer; initialized randomly between -0.5 and +0.5; n_output rows; n_hidden columns; each column represents weights connected to one hidden node # # self.hidden_bias=np.zeros(n_hidden) #hidden layer bias; initialized to zero vector with length=n_hidden # self.output_bias=np.zeros(n_output) #output layer bias; initialized to zero vector with length=n_output # # ============================================================================= if __name__ == '__main__': <|file_sep|># -*- coding: utf-8 -*- """ Created on Sun Oct 14 12:26:49 2018 @author: amitsinghrawat """ from __future__ import division class Perceptron(object): if __name__ == '__main__': <|file_sep|># -*- coding: utf-8 -*- """ Created on Sun Oct 14 12:19:16 2018 @author: amitsinghrawat """ import numpy as np class Perceptron(object): if __name__ == '__main__': <|repo_name|>amitksr/Learning<|file_sep|>/DataScienceFromScratch/Chapter_7/7_6.py import math def entropy(p): if p==0 or p==1: return 0 return - p*math.log(p,2) - (1-p)*math.log((1-p),2) def partition_entropy(subsets): total_count=sum([len(subset) for subset in subsets]) return sum([len(subset)/total_count * entropy(count_positives(subset)/len(subset)) for subset in subsets]) def count_positives(subset): return len([label for _,label in subset if label==+1]) def partition_classes(rows,column): t={} for row in rows: value=row[column] if value not in t: t[value]=[] t[value].append(row) return list(t.values()) def build_tree(rows,scoref=entropy): if len(rows)==0: return DecisionNode() current_score=scoref(rows) best_gain=0.0 best_criteria=None best_sets=None column_count=len(rows[0])-1 for col in range(0,column_count): column_values={} for row in rows: column_values[row[col]]=1 for value in column_values.keys(): (set_true,set_false)=divideset(rows,col,value) p=float(len(set_true))/len(rows) gain=current_score-p*scoref(set_true)-(1-p)*scoref(set_false) if gain>best_gain and len(set_true)>0 and len(set_false)>0: best_gain=gain best_criteria=(col,value) best_sets=(set_true,set_false) class DecisionNode(object): if __name__ == '__main__': dataset=[[20,"M",10,"Y"], [30,"F",20,"N"], [40,"M",10,"Y"], [50,"M",20,"N"], [60,"M",30,"Y"], [70,"F",20,"Y"]] tree=build_tree(dataset)<|repo_name|>amitksr/Learning<|file_sep|>/DataScienceFromScratch/Chapter_7/7_7.py from __future__ import division class Perceptron(object): if __name__ == '__main__': <|repo_name|>amitksr/Learning<|file_sep|>/README.md # Learning Repository containing code from various books I am reading. <|repo_name|>amitksr/Learning<|file_sep|>/DataScienceFromScratch/Chapter_7/7_4.py import math def entropy(p): if p==0 or p==1: return 0 return - p*math.log(p,2) - (1-p)*math.log((1-p),2) def count_positives(rows): count=len([label for _,label in rows if label==+1]) return count def count_negatives(rows): count=len([label for _,label in rows if label==-1]) return count def partition_entropy(true_rows,false_rows): total_count=count_positives(true_rows)+count_negatives(false_rows) p=float(count_positives(true_rows))/total_count result=p*entropy(p)+(1-p)*entropy(1-p) return result if __name__ == '__main__': dataset=[[20,-100,+100,-200,+100], [30,-200,+300,-400,+200], [40,-300,+300,-500,+300], [50,-400,+400,-600,+400], [60,-500,+500,-700,+500], [70,-600,+600,-800,+600]] print(partition_entropy(dataset[:3],dataset[3:])) print(entropy(count_positives(dataset[:3])/len(dataset[:3]))) print(entropy(count_positives(dataset[3:])/len(dataset[3:])))gicoprotective effects against kidney injury induced by cisplatin treatment. 9: Methods: C57BL/6 mice were treated with cisplatin at a dose of 15 mg/kg body weight intraperitoneally once weekly three times consecutively with or without treatment of Astragaloside IV at a dose of either low dose (4 mg/kg body weight/day) or high dose (10 mg/kg body weight/day) orally once daily from day −4 through day +21 relative to cisplatin administration. 10: Results: Cisplatin treatment significantly increased serum creatinine levels from day +7 after cisplatin administration compared with those before cisplatin administration (P <  0.05). Cisplatin treatment also increased serum levels of BUN from day +7 after cisplatin administration compared with those before cisplatin administration (P <  0.05). In addition, cisplatin treatment significantly increased urinary levels of KIM-1 from day +7 after cisplatin administration compared with those before cisplatin administration (P <  0.05). Furthermore, cisplatin treatment increased renal tissue damage score from day +7 after cisplatin administration compared with those before cisplatin administration (P <  0.05). Treatment with Astragaloside IV significantly attenuated these changes induced by cisplatin treatment. 11: Conclusions: Our findings indicate that Astragaloside IV possesses renoprotective effects against kidney injury induced by cisplatin treatment. 12: ## Background 13: Cis-diamminedichloroplatinum II (cisplatin) is one of the most commonly used chemotherapeutic agents against many solid tumors such as testicular cancer [[11]]. However, its use is often limited by its nephrotoxicity [[11]]. Cisplatin nephrotoxicity is characterized by acute renal failure which is manifested by oliguria/anuria [[25]], elevated blood urea nitrogen (BUN) level [[4]], elevated serum creatinine level [[4]], tubular necrosis [[29]] and tubular obstruction [[24]]. 14: Several mechanisms have been proposed to explain cisplatin-induced nephrotoxicity including reactive oxygen species formation [[12], [17]], inflammation [[6]], apoptosis [[14]] and mitochondrial dysfunction [[26]]. Currently there are no effective treatments available to prevent or reverse the nephrotoxicity caused by cisplatin chemotherapy. 15: Astragalus