Angel City FC (w) vs Kansas City Current (w) – Betting Analysis

Angel City FC vs Kansas City Current Match Analysis

Expert Overview

The upcoming match between Angel City FC and Kansas City Current promises to be an intriguing encounter. Both teams have shown strong recent performances, with Angel City FC having a solid home record and Kansas City Current displaying resilience in away games. Key tactical considerations include Angel City’s aggressive pressing and Kansas City’s counter-attacking prowess. Injuries and suspensions could play a crucial role, with Angel City missing a key midfielder, while Kansas City has a fully fit squad. The match tempo is expected to be high, with both teams likely to push for goals early in the game.

The match is set to unfold at a fast pace, with both teams aiming to capitalize on early opportunities. Given their recent form, a high-scoring game is anticipated, making it crucial for bettors to consider various betting segments carefully.

Match Result (1X2)

Data Signals

Angel City FC has been dominant at home, winning 60% of their matches this season. Kansas City Current, on the other hand, has shown consistency in away games, securing victories in 55% of their outings. The predicted odds reflect these trends, with the home team slightly favored.

Risk Factors

The absence of a key midfielder for Angel City could disrupt their midfield control, while Kansas City’s ability to exploit counter-attacks remains a threat. Weather conditions and referee decisions could also influence the match outcome.

Recommended Picks

Considering the data signals and risk factors, betting on Angel City FC to win seems prudent. However, given Kansas City’s counter-attacking strength, placing a side bet on both teams scoring could also be advantageous.

Draw No Bet

Data Signals

Historically, draws have occurred in 25% of matches between these teams. The current form suggests that while a draw is possible, it is less likely than in previous encounters.

Risk Factors

The potential for early goals could reduce the likelihood of a draw. Additionally, tactical adjustments by either team could skew the result away from a stalemate.

Recommended Picks

Given the lower probability of a draw and the teams’ attacking tendencies, avoiding Draw No Bet bets might be wise. Instead, focusing on specific goal-related bets could yield better returns.

Both Teams To Score (BTTS)

Data Signals

Both teams have shown an average of 2.81 goals scored per match recently, indicating a high likelihood of both teams finding the back of the net. This trend supports the BTTS market.

Risk Factors

Defensive lapses or unexpected tactical changes could affect scoring patterns. However, given both teams’ offensive capabilities, BTTS remains a strong bet.

Recommended Picks

Betting on Both Teams To Score is recommended based on current form and historical data. This segment offers attractive odds and aligns well with the expected match dynamics.

Total Goals (Over/Under)

Data Signals

With an average total of 3.29 goals per game between these teams, betting on Over 1.5 Goals seems logical. Historical data supports this expectation, given both teams’ attacking styles.

Risk Factors

Potential weather disruptions or defensive improvements could impact goal totals. However, the trend towards high-scoring games makes Over 1.5 Goals a compelling choice.

Recommended Picks

Opting for Over 1.5 Goals is advisable, considering the teams’ recent performances and historical head-to-head data. This bet aligns well with the expected match tempo and scoring opportunities.

Asian Handicap

Data Signals

Angel City FC’s home advantage and recent form suggest they are likely to win by at least one goal margin. The Asian Handicap market reflects this with favorable odds for the home team.

Risk Factors

Kansas City’s counter-attacking ability poses a risk to a comfortable home win. Additionally, any tactical surprises from Kansas City could alter expected outcomes.

Recommended Picks

Betting on Angel City FC with an Asian Handicap of -0.5 is recommended. This bet balances the home team’s advantage with Kansas City’s potential to score.

Player Props (optional)

Data Signals

Key players from both teams have been in excellent form recently. Angel City’s top scorer has netted 15 goals this season, while Kansas City’s main playmaker has contributed significantly to their attacking play.

Risk Factors

Injuries or tactical changes could limit player involvement or effectiveness. Monitoring pre-match reports will be crucial for informed betting decisions.

Recommended Picks

Betting on Angel City’s top scorer to score anytime offers good value given their attacking prowess at home. Similarly, backing Kansas City’s playmaker to assist can capitalize on their creative threat.

Corners (optional)

Data Signals

Both teams average around 6 corners per game in recent fixtures, suggesting that betting on Over 5 corners for each team is viable.

Risk Factors

Defensive strategies or game tempo changes could reduce corner opportunities. However, given both teams’ attacking styles, corner bets remain attractive.

Recommended Picks

Opting for Over 5 corners for each team aligns with their offensive approaches and recent trends. This segment offers solid betting opportunities based on current data.

Cards (optional)</h1

Data Signals</h4

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Risk Factors
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The intensity of play and referee tendencies can influence card issuance unpredictably.

[0]: import numpy as np
[1]: import os
[2]: import matplotlib.pyplot as plt
[3]: import h5py
[4]: import math
[5]: import torch.nn as nn
[6]: import torch.nn.functional as F

[7]: class h5Dataset:
[8]: def __init__(self,
[9]: train=True,
[10]: data_path=None,
[11]: batch_size=10,
[12]: random_seed=123,
[13]: single_im_size=224):
[14]: if train:
[15]: self.data_path = data_path + “train.hdf5”
[16]: else:
[17]: self.data_path = data_path + “test.hdf5”
[18]: self.batch_size = batch_size
[19]: self.single_im_size = single_im_size
[20]: self.num_batches = int(math.ceil(10000 / batch_size))

[21]: # Precompute size information:

[22]: # Get image sizes from HDF5 file:

[23]: hf = h5py.File(self.data_path,’r’)

[24]: # print(list(hf.keys()))

[25]: image_size_arr = hf[‘images’][0].shape

[26]: # print(image_size_arr)

[27]: self.image_width = image_size_arr[-1]

[28]: self.image_height = image_size_arr[-2]

[29]: def __iter__(self):

[30]: return self

***** Tag Data *****
ID: 1
description: Class `h5Dataset` that handles loading images from an HDF5 file and preparing
them in batches for training/testing neural networks.
start line: 7
end line: 28
dependencies: []
context description: This class encapsulates logic for reading image data stored in
HDF5 format and preparing it into batches which can then be used for training or
testing neural networks using PyTorch or similar frameworks.
algorithmic depth: 4
algorithmic depth external: N
obscurity: 4
advanced coding concepts: 4
interesting for students: 4
self contained: N

*************
## Suggestions for complexity

1. **Dynamic Batch Size Adjustment**: Implement logic that dynamically adjusts the batch size based on available memory during runtime without restarting the process.

2. **Multi-threaded Data Loading**: Integrate multi-threading capabilities to load images concurrently from multiple HDF5 files stored across different directories.

3. **Augmentation Pipeline**: Add an augmentation pipeline that applies random transformations (e.g., rotation, scaling) to each image before batching.

4. **Custom Sampling Strategy**: Implement a custom sampling strategy that prioritizes certain images based on metadata (e.g., image quality scores) rather than random sampling.

5. **Memory Mapping Optimization**: Optimize memory usage by using memory mapping techniques for reading large HDF5 files without loading them entirely into RAM.

## Conversation

i need help with code [SNIPPET]

Sure! What specifically do you need help with regarding this code?

i want add dynamic batch size adjustment how i do

To implement dynamic batch size adjustment based on available memory during runtime without restarting the process, you can modify your class like this:

Firstly, you’ll need to monitor available memory periodically and adjust `self.batch_size` accordingly:

python
import psutil

class h5Dataset:
def __init__(self,
train=True,
data_path=None,
batch_size=10,
random_seed=123,
single_im_size=224):
if train:
self.data_path = data_path + “train.hdf5”
else:
self.data_path = data_path + “test.hdf5”
self.batch_size = batch_size
self.single_im_size = single_im_size

# Precompute size information:
hf = h5py.File(self.data_path,’r’)
image_size_arr = hf[‘images’][0].shape
self.image_width = image_size_arr[-1]
self.image_height = image_size_arr[-2]

# Initialize memory usage monitoring parameters:
self.min_memory_fraction = 0.1

def adjust_batch_size(self):
available_memory = psutil.virtual_memory().available / (1024 ** 2) # Convert bytes to MB

# Calculate new batch size based on available memory:
approx_memory_per_image_mb = (self.image_width * self.image_height * 4) / (1024 ** 2) # Assuming float32

max_batch_size_based_on_memory = int((available_memory * self.min_memory_fraction) / approx_memory_per_image_mb)

if max_batch_size_based_on_memory > self.batch_size:
self.batch_size = max_batch_size_based_on_memory

def get_next_batch(self):
self.adjust_batch_size()
# Load next batch using adjusted batch size…

You would call `adjust_batch_size()` before loading each new batch within your data loading method.

Does this help? Do you need more detailed implementation?
1: DOI: 10.4102/sajid.v35i1.258
# Occurrence of SARS-CoV-2 variants of concern in KwaZulu-Natal Province during April – June 2021 using targeted sequencing of SARS-CoV-2 genomes from wastewater samples collected across Durban Metro Region South Africa
2: Authors: Siyabonga Nkambule-Maphumulo, Bulelani Radebe-Madikane, Zintle Mdluli-Mafuyaani , Thokozani Hadebe-Mkhwanazi , Sipho Zuma-Dlamini , Nontokozo Khumalo-Nkosi , Busisiwe Khumalo-Khumalo , Khanyisile Gwebu-Lasana , Nomawethu Makhoba-Nkosi , Sibongile Mkhwanazi-Dladla , et al.
3: Journal: Southern African Journal of Infectious Diseases
4: Date: 25 February 2020
5: Keywords: SARS-CoV-2 variant tracking; wastewater surveillance; wastewater surveillance; wastewater surveillance; wastewater surveillance; wastewater surveillance; wastewater surveillance; wastewater surveillance; wastewater surveillance; wastewater surveillance; wastewater surveillance; wastewater surveillance; wastewater surveillance; wastewater surveillance; wastewater surveillance; wastewater surveillance.

6: ## Abstract

7: **Background:**
8: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), causing coronavirus disease-2019 (COVID-19), continues to pose significant global public health challenges through its capacity to rapidly evolve variants of concern (VoC). Wastewater-based epidemiology offers an opportunity to track SARS-CoV-2 VoC through non-invasive sampling at scale.

9: **Aim:**
10: The aim was to identify SARS-CoV-2 VoC from April – June 2021 using targeted sequencing.

11: **Setting:**
12: This study was conducted across eThekwini Municipality’s Durban Metro Region comprising four districts namely Umlazi district council area (UDCA), Inanda district council area (IDCA), KwaDukuza district council area (KDCA) and eThekwini district council area (EDCA).

13: **Methods:**
14: A total of six samples were collected weekly at each study site over three months from April – June inclusive at six different sites across Durban Metro Region comprising two sites per district council area.

15: **Results:**
16: During April – June inclusive samples were collected from six sites across four districts within Durban Metro Region comprising two sites per district council area resulting in total of seventy-eight samples analysed using targeted sequencing methods which enabled identification of B117 variant as predominant strain detected during April – June inclusive.

17: **Conclusion:**
18: Wastewater-based epidemiology provides an opportunity to track SARS-CoV-2 VoC through non-invasive sampling at scale thereby enabling public health authorities’ decision-making regarding mitigation strategies.

19: ## Introduction

20: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causing coronavirus disease-2019 (COVID-19) continues to pose significant global public health challenges through its capacity to rapidly evolve variants of concern (VoC). These VoC are characterised by enhanced transmissibility associated with mutations occurring in spike protein receptor-binding domain [1] thereby increasing viral transmission among individuals exposed within communities [6].

21: A number of variants have been identified globally since initial detection [8]. Of concern are variants first identified in Brazil [9], South Africa [10] and United Kingdom [11] designated P1/Brazil variant [9], B1351/South Africa variant [10] and B117/United Kingdom variant [11] respectively due their enhanced transmissibility which led these countries experiencing increased COVID-19 infection rates during first quarter of year 2021 [12]. In addition these variants were associated with decreased vaccine effectiveness [15].

22: Wastewater-based epidemiology offers an opportunity to track SARS-CoV-2 VoC through non-invasive sampling at scale [17]. Studies conducted in Canada [18] United Kingdom [19] France [20] Italy [21] Sweden [22] Israel [23] Belgium [24], Denmark [25] Netherlands [26], Germany [27], Australia [28], South Korea [29] Switzerland [30], United States of America [31] have demonstrated feasibility of detecting SARS-CoV-2 RNA using targeted sequencing methods enabling identification of VoC within communities served by wastewater treatment plants including informal settlements where limited access exists due poor healthcare infrastructure.

23: Identification VoC through targeted sequencing enables public health authorities’ decision-making regarding mitigation strategies including social distancing measures such as lockdowns school closures etc., vaccination campaigns targeted towards vulnerable populations such as elderly individuals living alone etc., contact tracing efforts aimed at limiting spread among close contacts exposed individuals infected individuals etc., antiviral drug development aimed reducing severity illness associated infection cases etc., thereby enabling timely implementation appropriate measures necessary reducing impact pandemic communities affected worldwide.

24: ## Aim

25: The aim was to identify SARS-CoV-2 VoC from April – June inclusive using targeted sequencing methods.

26: ## Setting and design

27: This study was conducted across eThekwini Municipality’s Durban Metro Region comprising four districts namely Umlazi district council area (UDCA), Inanda district council area (IDCA), KwaDukuza district council area (KDCA) and eThekwini district council area (EDCA).

28: ## Methods

29: ### Study design

30: A longitudinal study design was employed collecting samples weekly over three months from April – June inclusive.

31: ### Sample collection

32: A total of six samples were collected weekly at each study site over three months from April – June inclusive at six different sites across Durban Metro Region comprising two sites per district council area.

33: ### Sampling procedure

34: Samples were collected using standard operating procedures outlined by World Health Organization guidelines ensuring consistency across all sites including sampling point location depth sample volume temperature sample collection time storage conditions sample transportation conditions sample processing conditions sample storage conditions sample quality control measures etc., ensuring reliability validity results obtained analyses conducted hereinbelow described below.

35: ### Sample processing methods employed hereinbelow described below:

36: Samples were processed using magnetic bead-based nucleic acid extraction method followed by reverse transcription quantitative polymerase chain reaction targeting spike protein receptor-binding domain gene region enabling identification presence absence specific mutations associated VoC hereinbelow described below.

37: ### Sequencing methods employed hereinbelow described below:

38: Samples were sequenced using Illumina MiSeq platform employing targeted sequencing method designed detect specific mutations associated VoC hereinbelow described below enabling identification presence absence specific mutations associated VoC hereinbelow described below.

39: ### Data analysis methods employed hereinbelow described below:

40: Sequence reads obtained Illumina MiSeq platform were aligned reference genome Wuhan-Hu-1 strain NC_045512v2 obtained National Center Biotechnology