Introduction to Handball Superliga Poland
The Handball Superliga Poland is the pinnacle of handball competition in Poland, showcasing the most skilled teams and players in the country. With a rich history and a passionate fan base, this league is a hotbed for thrilling matches and intense rivalries. As the season progresses, fans eagerly anticipate daily updates on fresh matches, complete with expert betting predictions to enhance their viewing experience.
Stay ahead of the game with our comprehensive coverage, providing you with the latest insights and analysis. Whether you're a seasoned fan or new to the sport, our platform offers everything you need to stay informed and engaged with every match. From detailed team profiles to in-depth tactical breakdowns, we ensure you have all the tools to make informed predictions and enjoy the excitement of the Superliga.
Understanding the League Structure
The Handball Superliga Poland is structured into two main phases: the regular season and the playoffs. During the regular season, teams compete in a round-robin format, facing each opponent twice—home and away. This ensures a fair and balanced competition, where every match counts towards securing a spot in the playoffs.
As the regular season concludes, the top eight teams advance to the playoffs. These teams face off in a knockout format, culminating in a final that determines the champion of Poland. The intensity of these matches is unmatched, as teams vie for glory on their home courts.
Key Teams and Players to Watch
The Superliga features some of Poland's most storied clubs, each with its own unique history and fan base. Among them are:
- PGSM Zaglebie Lubin: Known for their strong defense and tactical prowess, Zaglebie Lubin consistently challenges for top honors.
- PGE Vive Kielce: A powerhouse in Polish handball, Kielce boasts a roster filled with international stars and has won multiple national titles.
- Poznan Breakers: With a focus on youth development, Poznan Breakers have emerged as a formidable force in recent seasons.
- Jednostka Samochodow Szczecin: Renowned for their dynamic offense, Szczecin continues to be a threat in every match they play.
Key players to watch include:
- Bartosz Jurecki: A prolific scorer for PGE Vive Kielce, Jurecki's agility and precision make him a standout performer.
- Krzysztof Lijewski: Known for his leadership on the court, Lijewski's experience is invaluable to his team's success.
- Marcin Lijewski: With his incredible speed and defensive skills, Marcin is a key player for his team.
Daily Match Updates and Expert Analysis
Our platform provides daily updates on all matches in the Handball Superliga Poland. Each day brings new opportunities for teams to make their mark, and we ensure you have all the information you need at your fingertips.
Our expert analysts offer detailed breakdowns of each game, highlighting key moments and strategic decisions that could influence the outcome. Whether it's a last-minute goal or a crucial defensive play, our coverage captures every exciting detail.
Betting Predictions: Enhancing Your Viewing Experience
Betting adds an extra layer of excitement to watching handball matches. Our expert betting predictions are designed to help you make informed decisions based on thorough analysis and insights.
- Match Odds: We provide comprehensive odds for each match, allowing you to gauge potential outcomes based on current form and historical data.
- Tips from Experts: Our team of seasoned analysts offers betting tips grounded in extensive research and experience.
- Live Updates: Stay updated with live betting odds as matches progress, ensuring you never miss an opportunity to place informed bets.
In-Depth Team Profiles
To help you better understand the dynamics of each team, we offer detailed profiles that cover their history, key players, coaching staff, and recent performances. These profiles provide valuable context that can enhance your appreciation of each match.
- PGSM Zaglebie Lubin: Explore their journey from humble beginnings to becoming one of Poland's top teams.
- PGE Vive Kielce: Delve into their dominance in Polish handball and their impact on international competitions.
- Poznan Breakers: Learn about their commitment to nurturing young talent and how it has shaped their competitive edge.
- Jednostka Samochodow Szczecin: Discover their strategic approach that has led to consistent success over the years.
Tactical Breakdowns: Understanding Game Strategies
Handball is as much about strategy as it is about skill. Our tactical breakdowns offer insights into how teams approach each game, from offensive formations to defensive setups. By understanding these strategies, you can gain a deeper appreciation of the game's complexities.
- Offensive Tactics: Analyze how teams create scoring opportunities through quick passes and strategic positioning.
- Defensive Strategies: Learn about various defensive formations used to thwart opponents' attacks.
- In-Game Adjustments: Discover how coaches adapt their tactics during matches to counter opponents' strengths.
The Thrill of Live Matches: What Makes Them Unmissable
There's something uniquely exhilarating about watching handball live. The fast pace of the game, combined with high stakes and passionate fans, creates an atmosphere unlike any other sport. Our coverage ensures you don't miss any of the action with live updates and real-time commentary.
- Action-Packed Gameplay: Experience the rapid transitions from defense to offense that define handball's thrilling nature.
- Fan Engagement: Join a community of passionate fans who share your enthusiasm for every goal scored and every save made.
- Celebratory Moments: Relive iconic plays and memorable victories that capture the spirit of Polish handball.
Making Informed Predictions: Tips from Experts
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 28 16:06:26 2016
@author: Tom
"""
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
def min_max_normalization(x):
x = np.array(x)
if len(x.shape) ==1:
x = x.reshape(-1)
x_min = x.min()
x_max = x.max()
return (x - x_min) / (x_max - x_min)
elif len(x.shape) ==2:
x_min = x.min(axis=0)
x_max = x.max(axis=0)
return (x - x_min) / (x_max - x_min)
def logit_normalization(x):
return np.log(1+x)/(1+x)
def softmax(x):
if len(x.shape) ==1:
exp_x = np.exp(x)
return exp_x/np.sum(exp_x)
elif len(x.shape) ==2:
exp_x = np.exp(x)
return exp_x/np.sum(exp_x,axis=1).reshape(-1,len(x[0]))
def softmax_gradient(y,x):
# assert y.shape[0] ==x.shape[0]
# assert y.shape[1] ==x.shape[1]
# print(y.shape)
# print(x.shape)
# print(y-x)
# print((y-x).T.dot(y-x))
# print((y-x).T.dot(y-x)/y.shape[0])
# print(np.eye(len(y[0])).dot(y.T))
# print((y-x).T.dot(y-x)/y.shape[0]-np.eye(len(y[0])).dot(y.T)/y.shape[0])
# print(np.eye(len(y[0])).dot(np.ones(y.shape)))
# print(np.ones(y.shape).T.dot(np.ones(y.shape))/y.shape[0])
# print((y-x).T.dot(y-x)/y.shape[0]-np.eye(len(y[0])).dot(np.ones(y.shape))/y.shape[0])
# print(softmax_gradient_numpy(softmax_gradient_torch(softmax_torch(torch.from_numpy(x)),torch.from_numpy(y)).detach().numpy(),x,y))
return (y-x).T.dot(y-x)/y.shape[0]-np.eye(len(y[0])).dot(np.ones(y.shape))/y.shape[0]
def softmax_gradient_torch(ypred,ytrue):
# assert ypred.dim() ==2
# assert ytrue.dim() ==2
# assert ypred.size(0) == ytrue.size(0)
# assert ypred.size(1) == ytrue.size(1)
return (ypred-ytrue).t().mm(ypred-ytrue)/ypred.size(0)-torch.eye(len(ypred[0]),device=ypred.device).mm(torch.ones_like(ypred).t())/ypred.size(0)
def softmax_torch(x):
# assert x.dim() ==2
# dim_1_size = x.size(1)
# exp_x = torch.exp(x)
# sum_exp_x = torch.sum(exp_x,dim=1).reshape(-1,dim_1_size)
# return exp_x/sum_exp_x
def softmax_gradient_numpy(p,x,y):
return (y-x).T.dot(p)-np.outer(np.ones(len(p)),np.sum(p,axis=0))
def softmax_gradient_manual(p,x,y):
return p*(np.diag(np.sum(p,axis=0))-np.outer(p,p.T)).dot(np.ones((len(p),len(p))))-(y-x)*np.outer(np.ones(len(p)),np.sum(p,axis=0))
def softmax_gradient_manual_3D(p,x,y):
# assert p.ndim ==3
# p_sum_axis_1 = np.sum(p,axis=1).reshape(-1,len(p[0]),len(p[0][0]))
# p_sum_axis_1_diag = np.empty_like(p_sum_axis_1,dtype=float)
# for i in range(len(p)):
# p_sum_axis_1_diag[i] = np.diagflat(p_sum_axis_1[i])
# print(i,p_sum_axis_1_diag[i])
# break
def test_softmax_gradient():
if __name__=='__main__':
<|file_sep|># -*- coding: utf-8 -*-
"""
Created on Wed Sep 28 16:06:26 2016
@author: Tom
"""
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from . import utils
class HiddenLayer(object):
def __init__(self,n_in,n_out,W=None,b=None,
activation='sigmoid'):
self.n_in = n_in
self.n_out = n_out
if W is None:
self.W = np.random.randn(n_in,n_out)*np.sqrt(2/(n_in+n_out))
#self.W = np.random.randn(n_in,n_out)*np.sqrt(6/(n_in+n_out))
#self.W = np.random.randn(n_in,n_out)*np.sqrt(6/n_in)
#self.W = np.random.randn(n_in,n_out)*np.sqrt(6/n_out)
#self.W = np.random.randn(n_in,n_out)*np.sqrt(6/n_in*n_out)
#self.W[np.abs(self.W)<10e-3] *=10e-3
#self.W[np.abs(self.W)>10e-3] *=10e-3
#self.W[np.abs(self.W)>10e-3] /=10e-3
else:
self.W = W
if b is None:
self.b = np.zeros(n_out,dtype=float)
else:
self.b = b
self.activation_str = activation
if activation=='sigmoid':
self.activation_forward_fcn,self.activation_backward_fcn=self.sigmoid_forward_fcn,self.sigmoid_backward_fcn
elif activation=='tanh':
self.activation_forward_fcn,self.activation_backward_fcn=self.tanh_forward_fcn,self.tanh_backward_fcn
elif activation=='relu':
self.activation_forward_fcn,self.activation_backward_fcn=self.relu_forward_fcn,self.relu_backward_fcn
elif activation=='softmax':
self.activation_forward_fcn,self.activation_backward_fcn=self.softmax_forward_fcn,self.softmax_backward_fcn
class MultiLayerPerceptron(object):
def __init__(self,n_ins,n_hiddens,n_outs,Ws=None,bias=True,
activations=['sigmoid','softmax'],
output_activation='softmax',
initial_weights=None,
use_batch_norm=False,
use_dropout=False,
dropout_p=10e-5):
self.n_ins = n_ins
self.n_hiddens = n_hiddens
self.n_outs = n_outs
self.use_bias=bias
self.activations=activations
self.output_activation=output_activation
if use_dropout:
assert dropout_p!=None,'Must specify dropout probability'
self.dropout_p=dropout_p
assert dropout_p<=1.,'Dropout probability must be less than or equal to one'
assert dropout_p>=0.,'Dropout probability must be greater than or equal to zero'
<|repo_name|>tommydang/DeepLearning<|file_sep|>/utils.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Aug 21st
@author: Tom Dang
"""
import numpy as np
import matplotlib.pyplot as plt
from scipy.special import expit as sigmoid
def sigmoid_prime(z):
return sigmoid(z)*(1-sigmoid(z))
def tanh_prime(z):
return (np.cosh(z))**(-2)
def relu_prime(z):
return z*z >0
def relu_derivative(z):
relu_derivative=np.zeros(z.shape,dtype=int)
relu_derivative[z >0]=1.
return relu_derivative
def relu_prime_derivative(z):
relu_prime_derivative=np.zeros(z.shape,dtype=int)
relu_prime_derivative[z >0]=z[z >0]
return relu_prime_derivative
<|file_sep|># DeepLearning
Repository containing code for my Deep Learning project.
<|repo_name|>tommydang/DeepLearning<|file_sep|>/NeuralNet.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Aug 21st
@author: Tom Dang
"""
import numpy as np
import matplotlib.pyplot as plt
from scipy.special import expit as sigmoid
class NeuralNet:
def __init__(self,X,Y,n_hidden_units=[10],activation='sigmoid'):
self.X=X
self.Y=Y
self.n_inputs=X.shape[-1]
self.n_outputs=Y.shape[-1]
self.hidden_layers=n_hidden_units
self.n_hidden_layers=len(self.hidden_layers)
if activation=='sigmoid':
self.activation=sigmoid
self.activation_prime=sigmoid_prime
elif activation=='tanh':
self.activation=np.tanh
self.activation_prime=tanh_prime
