First League Women stats & predictions
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Upcoming Excitement: Women's First League Croatia
The Croatian Women's First League is gearing up for another thrilling weekend of football. Fans across the country and beyond are eagerly anticipating the matches scheduled for tomorrow. With top teams vying for supremacy, the stakes couldn't be higher. This article provides expert betting predictions and insights into the key matchups that promise to deliver excitement and edge-of-the-seat action.
Match Highlights
Tomorrow's fixtures feature some of the most competitive teams in the league. Here's a breakdown of the key matches and what to expect from each:
Osijek Ladies vs. Split Eagles
This match is one of the most anticipated fixtures of the weekend. Osijek Ladies, known for their aggressive playing style, will face off against the defensively solid Split Eagles. Osijek has been in top form recently, boasting a winning streak that has boosted their confidence. Split Eagles, on the other hand, have shown resilience, often turning games around with their strategic play.
- Osijek Ladies: Known for their fast-paced attacks and strong midfield presence.
- Split Eagles: Renowned for their defensive solidity and counter-attacking prowess.
Rijeka Waves vs. Zagreb Titans
The clash between Rijeka Waves and Zagreb Titans is expected to be a tactical battle. Rijeka Waves have been impressive in their recent outings, with a focus on possession-based football. Zagreb Titans, however, are not to be underestimated, with their ability to capitalize on set-pieces and create scoring opportunities from tight situations.
- Rijeka Waves: Strong in possession and creative in attack.
- Zagreb Titans: Effective in set-pieces and resilient under pressure.
Dubrovnik Dolphins vs. Pula Sharks
Dubrovnik Dolphins and Pula Sharks are set to battle it out in what promises to be a closely contested match. Both teams have shown great potential this season, with Dubrovnik Dolphins excelling in home games and Pula Sharks displaying remarkable away performance.
- Dubrovnik Dolphins: Dominant at home with a passionate fanbase.
- Pula Sharks: Strong away record with consistent performances.
Betting Predictions
Betting enthusiasts have much to look forward to with tomorrow's fixtures. Here are some expert predictions based on current form, head-to-head records, and statistical analysis:
Osijek Ladies vs. Split Eagles
The betting odds favor Osijek Ladies slightly due to their recent form and home advantage. A predicted scoreline could be 2-1 in favor of Osijek Ladies, with both teams expected to score.
- Prediction: Osijek Ladies win (Odds: 1.75)
- Both Teams to Score: Yes (Odds: 1.90)
Rijeka Waves vs. Zagreb Titans
This match is expected to be tight, with a slight edge for Rijeka Waves due to their home advantage and recent performances. A possible scoreline could be 1-0 or 2-1 in favor of Rijeka Waves.
- Prediction: Rijeka Waves win (Odds: 2.10)
- Under 2.5 Goals: Likely (Odds: 1.85)
Dubrovnik Dolphins vs. Pula Sharks
A draw seems likely given both teams' current form and previous encounters. A predicted scoreline could be a 1-1 draw or a narrow win for either side.
- Prediction: Draw (Odds: 3.00)
- Both Teams to Score: Yes (Odds: 1.80)
In-Depth Team Analysis
To provide a comprehensive understanding of tomorrow's fixtures, let's delve deeper into the strengths and weaknesses of each team involved.
Osijek Ladies
Osijek Ladies have been a force to reckon with this season, thanks to their dynamic midfield and lethal forwards. Their ability to maintain high pressure throughout the game has been a key factor in their success.
- Strengths: High pressing game, strong midfield control, clinical finishing.
- Weakeness: Occasionally vulnerable on the counter-attack.
Split Eagles
Split Eagles have built their reputation on solid defense and strategic counter-attacks. Their ability to absorb pressure and hit opponents on the break makes them a tough opponent.
- Strengths: Strong defensive line, effective counter-attacks.
- Weakeness: Struggles with maintaining possession under pressure.
Rijeka Waves
Rijeka Waves' possession-based approach has yielded positive results this season. Their creative midfielders are adept at breaking down defenses and creating scoring opportunities.
- Strengths: Possession control, creative playmaking.
- Weakeness: Vulnerable to quick transitions by opponents.
Zagreb Titans
Zagreb Titans have shown remarkable resilience, often turning games around through effective set-piece execution and disciplined defending.
- Strengths: Set-piece efficiency, disciplined defense.
- Weakeness: Limited creativity in open play.
Dubrovnik Dolphins
Dubrovnik Dolphins thrive in home conditions, leveraging their passionate support to fuel their performances. Their attacking prowess has been instrumental in securing victories at home.
- Strengths: Home advantage, strong attacking line.
- Weakeness: Struggles away from home.
Pula Sharks
Pula Sharks have demonstrated consistency in away games, often surprising opponents with their disciplined approach and tactical awareness.
- Strengths: Consistent away performances, tactical discipline.
- Weakeness: Occasional lapses in concentration leading to goals conceded.
Tactical Insights
Tomorrow's matches will not only test the physical capabilities of the players but also their tactical acumen. Coaches will need to make strategic decisions that could tip the balance in their favor.
Tactics for Osijek Ladies vs. Split Eagles
Osijek Ladies are expected to dominate possession and apply constant pressure on Split Eagles' defense. Split Eagles will likely focus on absorbing pressure and exploiting any gaps left by Osijek's aggressive play through quick counter-attacks.
Tactics for Rijeka Waves vs. Zagreb Titans
Rijeka Waves will aim to control the game through possession and intricate passing sequences. Zagreb Titans will look to disrupt this flow by pressing high up the pitch and capitalizing on set-pieces.
Tactics for Dubrovnik Dolphins vs. Pula Sharks
Dubrovnik Dolphins will leverage their home advantage by pushing forward aggressively from the start. Pula Sharks will need to maintain composure and look for opportunities during transitions when Dubrovnik pushes numbers forward.
Past Performances: A Statistical Overview
An analysis of past performances provides valuable insights into how these teams might perform tomorrow. Here are some key statistics from recent matches:
Past Performance Metrics
| Team | Total Goals Scored This Season | Total Goals Conceded This Season | Average Possession (%) This Season | Average Shots per Game This Season | Average Pass Accuracy (%) This Season |
|---|---|---|---|---|---|
| Osijek Ladies | 25 | 12 | 58% | 12.5 | 85% |
| Split Eagles | 18 | 15 | 52% | 10.0 | 80% |
| Rijeka Waves | 22 | 14 | 60% | 11.0 | 83% |
| Zagreb Titans | 19 | 17 | 55% | 9.5 | 78% |
| Dubrovnik Dolphins meishu-xu/GraphSAGE<|file_sep|>/README.md
# GraphSAGE
The official implementation of GraphSAGE can be found [here](https://github.com/williamleif/graphsage)
This repository is just an experiment using PyTorch Geometric library.
## Dependencies
* [PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/)
* [torch_cluster](https://github.com/rusty1s/pytorch_cluster)
* [torch_sparse](https://github.com/rusty1s/pytorch_sparse)
## Dataset
This implementation only supports Cora dataset.
## Usage
bash
python train.py
## Performance
### Cora dataset
| Model | Accuracy |
|-------|----------|
| GCN | **81%** |
| GraphSAGE | **82%** |
<|repo_name|>meishu-xu/GraphSAGE<|file_sep|>/train.py
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch_geometric.datasets import Planetoid
from torch_geometric.utils import train_test_split_edges
from torch_geometric.nn import SAGEConv
class SAGE(torch.nn.Module):
def __init__(self,
num_features,
hidden_dim,
out_dim,
num_layers):
super(SAGE, self).__init__()
self.convs = nn.ModuleList()
self.convs.append(SAGEConv(num_features,
hidden_dim))
for _ in range(num_layers - 2):
self.convs.append(SAGEConv(hidden_dim,
hidden_dim))
self.convs.append(SAGEConv(hidden_dim,
out_dim))
def forward(self,
x,
adjs):
# `train_loader` computes the k-hop neighborhood of a batch of nodes,
# and returns, for each layer,
# a bipartite graph object, holding the bipartite edges `edge_index`.
# The bipartite graph object holds two objects called `row` and `col`
# representing connection from source nodes to target nodes.
# We can easily convert it into an edge_index matrix:
#
# edge_index = torch.stack([adj.row, adj.col], dim=0)
#
# The truth is `train_loader` also returns the corresponding `edge_index`
# matrices, but we can ignore those since we can easily obtain them
# from `adjs[row][col]` instead.
for i, (edge_index) in enumerate(adjs):
x = F.relu(self.convs[i](x, edge_index))
if i != len(adjs) - 1:
x = F.dropout(x,
p=0.5,
training=self.training)
return x.log_softmax(dim=-1)
def main():
dataset = Planetoid(root='/tmp/Cora',
name='Cora')
data = dataset[0]
data.train_mask = data.val_mask = data.test_mask = data.y == data.y
data = train_test_split_edges(data)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = SAGE(dataset.num_features,
hidden_dim=256,
out_dim=dataset.num_classes,
num_layers=2).to(device)
data = data.to(device)
optimizer = optim.Adam(model.parameters(),
lr=0.01,
weight_decay=5e-4)
def train():
model.train()
optimizer.zero_grad()
out = model(data.x.to(device), data.train_pos_edge_index.to(device))
loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask])
loss.backward()
optimizer.step()
def test():
model.eval()
logits = model(data.x.to(device), data.test_pos_edge_index.to(device))
test_mask = data.test_mask.to(device)
_, pred = logits[test_mask].max(dim=1)
correct = float(pred.eq(data.y[test_mask]).sum().item())
acc = correct / test_mask.sum().item()
print(f'Accuracy: {acc}')
t_total = int(100 / dataset.data['split'].sum(0)[dataset.data.train_mask].item() * len(dataset.data.train_mask) * dataset.data['split'].sum(0)[dataset.data.train_mask].item())
best_acc = test()
for epoch in range(200):
train()
if epoch % t_total == t_total - 1:
acc = test()
if acc > best_acc:
best_acc = acc
if __name__ == '__main__':
main()
<|file_sep|>// Copyright (c) Facebook, Inc. and its affiliates.
// All rights reserved.
//
// This source code is licensed under the BSD-style license found in the
// LICENSE file in the root directory of this source tree.
use std::collections::HashMap;
use std::fmt;
use std::ops::{DerefMut, Deref};
use crate::token::{TokenKind as Kind};
pub type IndexMap |