U19 League stats & predictions

Team A vs. Team B

Today's clash between Team A and Team B promises to be an exhilarating encounter. Both teams have shown great form recently, and this match could be a turning point in the league standings.

Betting Predictions

• Winning Team: Team A has been in excellent form, making them strong favorites to win.
• Top Scorer: Player X from Team A is expected to shine today, having scored multiple goals in recent matches.
• Total Goals: Over 2.5 goals are likely, given both teams' attacking prowess.

Team C vs. Team D

In another anticipated match, Team C faces off against Team D. This match is crucial for both teams as they aim to climb up the league table.

Betting Predictions

• Winning Team: Team D has been performing well defensively, but Team C's attack might just give them an edge.
• Draw Possibility: A draw seems likely given the balanced nature of both teams.
• Betting Tip: Consider betting on a low-scoring game, as both teams have solid defenses.

How can I stay updated with daily matches?

You can subscribe to our newsletter or follow us on social media for real-time updates and match highlights.

Where can I find expert betting predictions?

Our website features daily expert predictions with detailed analysis for each match in the U19 League Ukraine.

Are there any live streaming options?

Sure! We provide links to live streams where available, so you don't miss any of the action.

The Tactical Battle: Team A vs. Team B

This match is not just about skill but also about tactics. Both teams have unique strategies that could determine the outcome of the game. Let's delve deeper into their tactical approaches:

• Team A's Strategy: Known for their aggressive attacking play, Team A relies on quick transitions and pressing high up the pitch. Their forwards are constantly looking for spaces behind the defense, making them dangerous on counter-attacks.
• Team B's Defense: In contrast, Team B employs a compact defensive structure aimed at nullifying opposition attacks. Their defenders are disciplined and work cohesively to maintain their shape, while their midfielders focus on breaking up play and launching counter-attacks of their own.
• Potential Game-Changer: The midfield battle will be crucial in this match. If Team A's midfielders can control possession and dictate the pace of the game, they could dominate proceedings. However, if Team B manages to disrupt their rhythm, they could capitalize on any mistakes made by Team A's forwards.

Betting Insights: Who Has the Edge?

• Odds Analysis: Current odds favor Team A slightly due to their recent form and attacking prowess. However, considering Team B's solid defense, a bet on under goals might be worth considering.
• Betting Strategy: Consider placing a back bet on Team A to win but hedge with an over/under bet on total goals being under 2.5. This strategy balances potential winnings while minimizing risk if the game remains tight defensively.
• Bonus Tip: Keep an eye on player performances during warm-ups; substitutions or changes in formation could signal tactical adjustments that might influence betting outcomes.

A Clash of Styles: Team C vs. Team D

This fixture promises an intriguing clash of styles as two well-matched teams go head-to-head. Here's what you need to know about their playing styles and how they might impact today's game:

• Team C's Possession Play: Known for their meticulous build-up play from the back, Team C focuses on maintaining possession and patiently waiting for openings in opposition defenses. Their full-backs often join attacks, providing width and creating numerical superiority down the flanks.
• Team D's High Pressing Game:In response to possession-based teams like Team C, Team D employs a high pressing strategy aimed at disrupting their build-up play early in their own half. By applying pressure immediately after losing possession, they hope to force errors and regain control quickly.
• yongzhonglee/torchlib<|file_sep|>/utils/visualize.py # Copyright (c) Facebook, Inc. and its affiliates. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import numpy as np import os.path as osp import torch from PIL import Image import matplotlib.pyplot as plt def visualize_images(data, name='data', save_dir='./tmp', nrow=8, normalize=True, range=None): """Save images into files. Args: data (torch.Tensor or list): Image tensor of shape (b*c*h*w) or (b*h*w*c). name (str): Name of saved image file. save_dir (str): Directory used to save images. nrow (int): Number of images displayed in each row of grid. normalize (bool): If True, shift image to range [0.,1.]. range (tuple): Min-max value used for normalization. Returns: files (list): Absolute paths of saved images. """ if isinstance(data[0], torch.Tensor): data = [img.cpu() if isinstance(img, torch.autograd.Variable) else img for img in data] # If range is not specified for normalization, # use min-max range from tensor. if normalize: min_val = min(img.min() for img in data) max_val = max(img.max() for img in data) range = range if range else (min_val.item(), max_val.item()) # Move tensors with shape [c,h,w] into [1,c,h,w] # so that torchvision function works. data = [img.unsqueeze(0) if img.dim() == len(data[0].shape) -1 else img for img in data] # Create grid image with torchvision function. out = torchvision.utils.make_grid(data, nrow=nrow, normalize=normalize, value_range=range) out = out.numpy().transpose((1,2,0)) elif isinstance(data[0], np.ndarray): # If image is uint8 type convert into float32 type. if data[0].dtype == np.uint8: data = [img.astype(np.float32) /255 for img in data] # If range is not specified for normalization, # use min-max range from tensor. if normalize: min_val = min(img.min() for img in data) max_val = max(img.max() for img in data) range = range if range else (min_val.item(), max_val.item()) data = [(img - min_val)/(max_val-min_val) *np.array(range)[np.newaxis,np.newaxis,:] + np.array(range)[np.newaxis,np.newaxis,:][::-1] for img in data] # Create grid image with torchvision function. out = make_grid(data, nrow=nrow) elif isinstance(data[0], Image.Image): # Convert PIL image into numpy array. data = [np.array(img) /255 for img in data] # If range is not specified for normalization, # use min-max range from tensor. if normalize: min_val = min(img.min() for img in data) max_val = max(img.max() for img in data) range = range if range else (min_val.item(), max_val.item()) data = [(img - min_val)/(max_val-min_val) *np.array(range)[np.newaxis,np.newaxis,:] + np.array(range)[np.newaxis,np.newaxis,:][::-1] for img in data] # Create grid image with torchvision function. out = make_grid(data, nrow=nrow) else: raise TypeError('Wrong type of input.') return save_image(out, osp.join(save_dir,name + '.png')) def make_grid(tensor_list,nrow=8): """Make grid image from tensor list. Args: tensor_list (list): List contains image tensors which has shape [c,h,w]. nrow (int): Number of images displayed per row. Returns: grid_img (numpy.ndarray): Grid image containing all tensors. Shape [b*c,H,W]. b:nrows * ncols H:h*nrow+padding*(nrows-1) W:w*ncols+padding*(ncols-1) padding: minimum size required between two images. c: number of channels. h,w: height,width respectively. padding: minimum size required between two images. It will be set by `get_padding()` function. nrows: number of rows required when arranging `tensor_list` into grid. It will be calculated by `nrow` argument. ncols: number of columns required when arranging `tensor_list` into grid. It will be calculated by `nrows` argument. H,W : height,width respectively. b : number of tensors contained by `tensor_list`. It will be calculated by `len(tensor_list)`. Note: This function assumes that tensors are numpy arrays which have shape [c,h,w]. c : number of channels h : height w : width h,w should be same among all tensors contained by `tensor_list`. All tensors should contain float32 type values ranging from [0.,1.]. All tensors should contain same number of channels c. """ # Calculate number of rows required when arranging `tensor_list` into grid b=len(tensor_list) nrows=int(np.ceil(b/nrow)) # Calculate number columns required when arranging `tensor_list` into grid ncols=min(b,nrow) # Calculate height & width respectively when arranging `tensor_list` into grid h=tensor_list[0].shape[1] w=tensor_list[0].shape[2] # Calculate padding size between two images when arranging `tensor_list` into grid padding=get_padding(h,w) H=h*nrows+padding*(nrows-1) W=w*ncols+padding*(ncols-1) # Create empty array containing all pixels contained by `tensor_list` grid_img=np.zeros([b*tensor_list[0].shape[0],H,W]) # Insert each tensor into empty array created above based on its position index within `tensor_list` k=0 y_incr=0 y_incr=padding x_incr=0 while k# Copyright (c) Facebook, Inc. and its affiliates. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import os.path as osp import time import torch import torch.nn.functional as F import torch.nn.parallel import numpy as np from .base_solver import BaseSolver class Solver(BaseSolver): def _train_epoch(self):