The Thrill of the Game: Football National League Championship New Zealand
The Football National League Championship in New Zealand is not just a tournament; it's a showcase of passion, skill, and strategy. With each match bringing fresh excitement and unpredictable outcomes, fans and experts alike are drawn to the thrill of the game. This championship is a battleground where teams from across New Zealand vie for supremacy, each match adding a new chapter to the storied history of New Zealand football.
For those who love the sport, staying updated with the latest matches and expert betting predictions is essential. The dynamic nature of the league ensures that there is always something new to discover, making it a perfect subject for enthusiasts and analysts. Whether you're a seasoned fan or new to the sport, the Football National League Championship offers something for everyone.
Understanding the League Structure
The New Zealand Football National League Championship is structured to provide an intense and competitive environment for clubs across the nation. Teams compete in various divisions, with promotion and relegation adding an extra layer of excitement. Each division is fiercely contested, with teams striving to outperform their rivals and secure their place at the top of the league table.
- Divisions: The league is divided into multiple tiers, ensuring that teams of all levels have the opportunity to compete.
- Promotion and Relegation: Teams can move up or down divisions based on their performance, keeping the competition fierce.
- Regular Season: The regular season consists of numerous matches where teams earn points for wins and draws.
- Playoffs: At the end of the season, top teams enter playoffs to determine the ultimate champions.
The Role of Expert Betting Predictions
Betting on football matches adds an extra dimension to watching the game. Expert predictions provide valuable insights that can enhance your betting experience. These predictions are based on extensive analysis of team performance, player statistics, historical data, and current form. By leveraging expert insights, bettors can make more informed decisions and increase their chances of success.
Expert betting predictions are updated daily to reflect the latest developments in the league. This includes changes in team line-ups, injuries, weather conditions, and other factors that can influence match outcomes. Staying informed with these updates ensures that bettors are always ahead of the curve.
Key Factors Influencing Match Outcomes
- Team Form: A team's recent performance can be a strong indicator of their potential in upcoming matches.
- Head-to-Head Record: Historical matchups between teams can provide insights into potential outcomes.
- Injuries and Suspensions: Key player absences can significantly impact a team's performance.
- Home Advantage: Playing at home often gives teams an edge due to familiar surroundings and supportive crowds.
- Tactical Changes: Coaches may alter their strategies based on their opponents' strengths and weaknesses.
Daily Match Updates: Stay Informed
Keeping up with daily match updates is crucial for fans and bettors alike. Each day brings new developments that can affect team dynamics and match predictions. By following these updates closely, you can stay informed about key events such as:
- Scores from previous matches
- Upcoming fixtures
- Player transfers and acquisitions
- Injury reports
- Coaching changes
The Importance of Statistical Analysis
Statistical analysis plays a pivotal role in understanding football matches. By examining data such as goals scored, possession percentages, shots on target, and defensive records, analysts can identify trends and patterns that may not be immediately apparent. This data-driven approach allows for more accurate predictions and deeper insights into team performances.
Advanced analytics tools are used by experts to process vast amounts of data quickly and efficiently. These tools help in creating models that predict match outcomes based on historical data and current trends. For bettors, understanding these statistics can be a game-changer, providing them with an edge over less informed competitors.
Betting Strategies for Success
Developing effective betting strategies is essential for anyone looking to succeed in sports betting. Here are some strategies that can help:
- Bankroll Management: Allocate your betting funds wisely to ensure long-term sustainability.
- Diversification: Spread your bets across different matches to minimize risk.
- Focused Betting: Concentrate on matches where you have strong insights or information.
- Analyzing Odds: Compare odds from different bookmakers to find the best value bets.
- Staying Updated: Keep abreast of the latest news and updates to make informed decisions.
The Impact of Weather Conditions
yuyang1234/belief_tracking<|file_sep|>/utils/evaluator.py
import numpy as np
from tqdm import tqdm
def eval_belief(belief_estimations: np.ndarray,
ground_truth: np.ndarray,
n_agents: int,
n_states: int,
n_timesteps: int,
k: int = None) -> float:
"""Evaluate belief estimation.
Args:
belief_estimations (np.ndarray): Estimated beliefs [n_timesteps x n_agents x n_states]
ground_truth (np.ndarray): Ground truth beliefs [n_timesteps x n_agents x n_states]
n_agents (int): Number of agents
n_states (int): Number of states
n_timesteps (int): Number of timesteps
k (int): Top-k evaluation
Returns:
float: Average KL divergence across all timesteps
Raises:
AssertionError: If dimensions do not match
"""
assert belief_estimations.shape == ground_truth.shape == (n_timesteps,n_agents,n_states)
kl_div = []
for t in tqdm(range(n_timesteps)):
for i in range(n_agents):
if k is not None:
estimated_beliefs = belief_estimations[t,i,:].argsort()[-k:][::-1]
true_beliefs = ground_truth[t,i,:].argsort()[-k:][::-1]
kl_div.append(np.sum(ground_truth[t,i,true_beliefs] * np.log(ground_truth[t,i,true_beliefs] / belief_estimations[t,i,true_beliefs])))
else:
kl_div.append(np.sum(ground_truth[t,i,:] * np.log(ground_truth[t,i,:] / belief_estimations[t,i,:])))
return np.mean(kl_div)
<|repo_name|>yuyang1234/belief_tracking<|file_sep|>/data_processing/observation_converter.py
import numpy as np
import json
from pathlib import Path
def get_observation_converter(data_dir: str,
observation_type: str) -> callable:
if observation_type == "discrete":
return lambda obs_list: [obs["state"] for obs_list in obs_list for obs in obs_list]
elif observation_type == "continuous":
return lambda obs_list: [obs["state"] + obs["covariance"] for obs_list in obs_list for obs in obs_list]
elif observation_type == "full":
def convert(obs_list):
observations = []
for t_obs in obs_list:
t_obs_converted = []
for obs in t_obs:
state = np.array(obs["state"])
covariance = np.array(obs["covariance"])
state_covariance = np.concatenate((state,covariance.flatten()))
t_obs_converted.append(state_covariance)
observations.append(t_obs_converted)
return observations
return convert
<|repo_name|>yuyang1234/belief_tracking<|file_sep|>/README.md
# Belief Tracking
## Installation
### Dependencies
Install dependencies:
bash
pip install -r requirements.txt
### Dataset
Download dataset from [here](https://drive.google.com/file/d/1E6wq-hGJi5WkKvZx5b7zXKXmKvYR5R3H/view?usp=sharing). Move it to `./data` folder.
### Run script
bash
python main.py --data_dir ./data --experiment_name my_experiment --n_agents=3 --n_states=10 --n_timesteps=1000 --batch_size=32 --learning_rate=0.001 --hidden_dim=128 --beta=0.0001 --gamma=0.0001 --epochs=10 --seed=42
To see more parameters run `python main.py -h`.
## Evaluation
To evaluate trained model run:
bash
python main.py --data_dir ./data --experiment_name my_experiment --n_agents=3 --n_states=10 --n_timesteps=1000 --batch_size=32 --learning_rate=0.001 --hidden_dim=128 --beta=0.0001 --gamma=0.0001 --epochs=10 --seed=42 --eval_only=True
<|repo_name|>yuyang1234/belief_tracking<|file_sep|>/main.py
import os
import argparse
from pathlib import Path
import torch
import numpy as np
from utils.data_generator import DataGenerator
from utils.evaluator import eval_belief
from models.rnn_model import RNNModel
def parse_args():
# Parameters related to training
parser = argparse.ArgumentParser(description="Belief Tracking")
parser.add_argument("--data_dir", type=str,
default="./data",
help="Data directory")
parser.add_argument("--experiment_name", type=str,
default="my_experiment",
help="Experiment name")
parser.add_argument("--n_agents", type=int,
default=3,
help="Number of agents")
parser.add_argument("--n_states", type=int,
default=10,
help="Number of states")
parser.add_argument("--n_timesteps", type=int,
default=1000,
help="Number of timesteps")
parser.add_argument("--batch_size", type=int,
default=32,
help="Batch size")
parser.add_argument("--learning_rate", type=float,
default=0.001,
help="Learning rate")
parser.add_argument("--hidden_dim", type=int,
default=128,
help="Hidden dimension")
parser.add_argument("--beta", type=float,
default=0.0001,
help="Beta coefficient")
parser.add_argument("--gamma", type=float,
default=0.0001,
help="Gamma coefficient")
parser.add_argument("--epochs", type=int,
default=10,
help="Number of epochs")
parser.add_argument("--seed", type=int,
default=None,
help="Seed")
# Parameters related to evaluation
parser.add_argument("--eval_only", action='store_true',
help="Only evaluate saved model")
# Observation related parameters
parser.add_argument("--observation_type", type=str,
choices=["discrete","continuous","full"],
default="discrete",
help="Observation type")
parser.add_argument("--observation_noise_std", type=float,
default=None)
# Transition related parameters
parser.add_argument("--transition_type", type=str,
choices=["discrete","continuous"],
default="discrete",
help="Transition type")
parser.add_argument("--transition_noise_std", type=float,
default=None)
args = parser.parse_args()
return args
def main():
args = parse_args()
if args.seed is not None:
torch.manual_seed(args.seed)
np.random.seed(args.seed)
torch.backends.cudnn.deterministic = True
# Create experiment directory
exp_dir = Path("experiments") / args.experiment_name
if not os.path.exists(exp_dir):
os.makedirs(exp_dir)
print(f"Saving results to {exp_dir}")
# Data generation
data_generator = DataGenerator(data_dir=args.data_dir)
train_data_generator = data_generator.generate_data(n_agents=args.n_agents,n_states=args.n_states,n_timesteps=args.n_timesteps,batch_size=args.batch_size)
test_data_generator = data_generator.generate_data(n_agents=args.n_agents,n_states=args.n_states,n_timesteps=args.n_timesteps,batch_size=args.batch_size,is_train=False)
# Model initialization
rnn_model = RNNModel(args.n_agents,args.n_states,args.hidden_dim,args.beta,args.gamma,args.observation_type,args.transition_type,args.observation_noise_std,args.transition_noise_std)
if torch.cuda.is_available():
rnn_model.cuda()
optimizer = torch.optim.Adam(rnn_model.parameters(),lr=args.learning_rate)
if args.eval_only:
# Load model parameters from saved file
rnn_model.load_state_dict(torch.load(os.path.join(exp_dir,"model.pt")))
rnn_model.eval()
# Evaluation
with torch.no_grad():
test_loss_batched_kl,test_loss_batched_kl_k,test_loss_batched_je,test_loss_batched_je_k,test_loss_full_kl,test_loss_full_kl_k,test_loss_full_je,test_loss_full_je_k = [],[],[],[],[],[],[],[]
for batch_id,(observations,beliefs_ground_truth) in enumerate(test_data_generator):
observations,beliefs_ground_truth = observations.cuda(),beliefs_ground_truth.cuda()
beliefs_estimation,knowledge_representation_batched,knowledge_representation_full,_ = rnn_model(observations)
beliefs_estimation_batched,beliefs_estimation_full = beliefs_estimation[:,:-1],beliefs_estimation[:,1:]
kl_div_batched_kl,k_l_div_batched_je,k_l_div_batched_je_kl,k_l_div_full_je,k_l_div_full_je_kl,batch_size_unpadded,knowledge_representation_unpadded_batched,knowledge_representation_unpadded_full,t_step_unpadded_mask,pad_mask_batched,pad_mask_full,knowledge_representation_masked_batched,knowledge_representation_masked_full,t_step_unpadded_mask_inverse,t_step_unpadded_mask_inverse_square,t_step_unpadded_mask_inverse_square_inverse,knowledge_representation_unpadded_batched_inverse,knowledge_representation_unpadded_full_inverse,beliefs_ground_truth_unpadded_batched,beliefs_ground_truth_unpadded_full,beliefs_estimation_batched_unpadded,beliefs_estimation_full_unpadded,batch_ids_with_pad,batch_ids_without_pad = rnn_model.compute_losses(beliefs_ground_truth,beliefs_estimation_batched,beliefs_estimation_full,knowledge_representation_batched,knowledge_representation_full,pad_mask=True)
test_loss_batched_kl.append(kl_div_batched_kl.item())
test_loss_batched_je.append(k_l_div_batched_je.item())
test_loss_batched_kl_k.append(k_l_div_batched_je_kl.item())
test_loss_full_je.append(k_l_div_full_je.item())
test_loss_full_kl.append(kl_div_batched_kl.item())
test_loss_full_kl_k.append(k_l_div_full_je_kl.item())
# Compute average loss per timestep across all batches
average_test_loss_per_timestep_per_epoch_batched_kl=test_loss_batched_kl[-1]/batch_size_unpadded/t_step_unpadded_mask.sum().item()
average_test_loss_per_timestep_per_epoch_batched_je=test_loss_batched_je[-1]/batch_size_unpadded/t_step_unpadded_mask.sum().item()
average_test_loss_per_timestep_per_epoch_batched_je_k=test_loss_batched_kl_k[-1]/batch_size_unpadded/t_step_unpadded_mask.sum().item()
average_test_loss_per_timestep_per_epoch_full_je=test_loss_full_je[-1]/batch_size_unpadded/t_step_unpadded_mask.sum().item()
average_test_loss_per_timestep_per_epoch_full_kl=test_loss_full_kl[-1]/batch_size_unpadded/t_step_unpadded_mask.sum().item()
average_test_loss_per_timestep_per_epoch_full_je_k=test_loss_full_kl_k[-1]/batch_size_unpadded/t_step_unpadded_mask.sum().item()
print(f"Test loss batch KL:{average_test_loss_per_timestep_per_epoch_batched_kl:.6f}, Test loss batch JE:{average_test_loss_per_timestep_per_epoch_batched_je:.6f}, Test loss batch JE K:{average_test_loss_per_timestep_per_epoch_batched_je_k:.6f}, Test loss full JE:{average_test_loss_per_timestep_per_epoch_full_je:.6f}, Test loss full KL:{average_test_loss_per_timestep_per_epoch_full_kl:.6f}, Test loss full JE K:{average_test_loss_per_timestep_per_epoch_full_je_k:.6f}")
print("Saving results...")
results_dict={"test_avg_losses":{"batch_KL":np.mean(test_loss_batched_kl),"batch_JE":np.mean(test_loss_batched_je),"batch_JE_K":np.mean(test_loss_batched_klk),"full_JE":np.mean(test_loss_full_je),"full_KL":np.mean(test_loss_full_kl),"full_JE_K":np.mean(test_loss_full_jek)}}
json.dump(results_dict,open(os.path.join(exp_dir,"results.json"),"w"),indent=True)
print("Results saved.")
print("Evaluating...")
with torch.no_grad():
test_losses_eval,test_losses_eval_top5,test_losses_eval_top10,test_losses_eval_top20,test_losses_eval_top50=[],[],[],[],[]
for batch_id,(observations,beliefs_ground_truth) in enumerate(test
