Strategic advocacy and advisory work in complex employment disputes, with particular expertise in discrimination, whistleblowing, TUPE, and appellate litigation.
Approach
Cases are won in cross-examination, not on spreadsheets. But the spreadsheets help. Cambridge doctorate, a decade in publishing, legal education in Namibia, speechwriting for a Japanese Ambassador: a different route to the Bar, and a different kind of advocate.
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Resources and tools designed for employment law teams
All tools, calculators, and research materials on this site are for educational and informational purposes only. They do not constitute legal advice. Always verify calculations and guidance against primary sources and consult with counsel for specific matters.
Get in Touch / Resources
For instructions or advisory work, contact me directly or via St Philips clerks. Access key documents here:
Legal Research Database
Searchable database of employment law authorities with full summaries, judicial analysis, and cross-referenced statutes.
Explore Database →Practice Calculators
Instant calculators for limitation periods, notice pay, quantum, redundancy, and holiday pay. Built for speed and accuracy.
View Tools →Strategic Advocacy
Complex discrimination, whistleblowing, TUPE, and appellate work. Court of Appeal experience. Available for trial and advisory.
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🧠 Experimental Tools
What Clients Say
A superb and effective trial counsel.
Extremely responsive, knowledgeable and delivers high quality advice in a client-friendly manner.
Excellent preparation and advocacy that secured a successful outcome in a complex multi-day hearing.
Knowledge Base
Explore key areas of employment law and leading authorities.
This knowledge base is for educational and informational purposes only. It does not constitute legal advice. Case summaries and legal principles are simplified for reference and may not reflect the most current legal developments. Always consult a qualified professional for specific legal guidance.
List of Issues Builder
Generate a structured Word document (.docx) containing the statutory legal tests for your chosen claims.
This is an experimental educational tool for illustrative purposes only. It does not constitute legal advice. The generated document contains abstract legal tests which must be populated with specific factual allegations. Always consult a qualified professional for specific legal guidance.
A Tribunal will likely reject a List of Issues that only recites the abstract legal tests. To be useful, you must edit the downloaded document to populate it with your specific factual allegations (e.g., dates of incidents, specific words used, names of comparators).
Select Claims & Jurisdictions
Recent Instructed Cases
News & Insights
Articles and insights on this site are for educational and informational purposes only. They do not constitute legal advice. Content may not reflect the most current legal developments. Always consult a qualified professional for specific legal guidance.
Video Insights
Short video commentaries on recent appellate decisions and key employment law developments.
Video content is for educational and informational purposes only. It does not constitute legal advice. Discussions of cases and legal principles are simplified for accessibility. Always consult a qualified professional for specific legal guidance.
Coming Soon
Video commentaries on significant EAT and Court of Appeal decisions will appear here. Subscribe to be notified when new content is published.
Holiday Pay
For irregular-hours workers (52-week reference period estimate).
This is a practice tool for illustrative purposes only. It does not constitute legal advice. Calculations are based on standard 52-week reference periods and may not account for complex patterns of work or specific contract terms. Always consult a qualified professional for specific legal guidance.
📐 Mathematical Basis
• $\bar{W}$ = Average weekly pay over reference period
• $P_i$ = Gross pay in week $i$
• $n$ = Number of weeks worked (max 52)
• 5.6 = Statutory annual leave entitlement in weeks
Limitation Date Calculator
Calculate the primary limitation date for Employment Tribunal claims, accounting for ACAS Early Conciliation extensions.
This is a practice tool for illustrative purposes only. It does not constitute legal advice. Limitation dates are complex and may be affected by specific facts not captured here. Missing a deadline has severe legal consequences. Always consult a qualified professional for specific legal guidance.
📐 Mathematical Basis
• The primary limitation period is 3 months less one day from the effective date of termination (EDT) or the date of the act.
• ACAS Early Conciliation "stops the clock" on this period, extending it as per statutory rules (see below).
- Employment Rights Act 1996, s.111(2) (Unfair Dismissal)
- Equality Act 2010, s.123 (Discrimination Claims)
- The Employment Act 2002 (Dispute Resolution) Regulations 2004 (for ACAS early conciliation rules)
Statutory Notice Pay Calculator
This is a practice tool for illustrative purposes only. It does not constitute legal advice. It calculates statutory minimum notice based on years of service and does not account for enhanced contractual notice periods. Always consult a qualified professional for specific legal guidance.
📐 Mathematical Basis
• $Y$ = Completed years of service
• Gross Weekly Pay is subject to statutory cap.
- Employment Rights Act 1996, s.86 (Statutory minimum period of notice)
- Employment Rights Act 1996, s.220-229 (Calculation of a week's pay, including statutory cap)
Redundancy Pay Calculator
This is a practice tool for illustrative purposes only. It does not constitute legal advice. It calculates statutory redundancy pay based on age, service and pay (capped). It does not account for enhanced contractual redundancy. Always consult a qualified professional for specific legal guidance.
📐 Mathematical Basis
• $S_{total}$ = Total completed years of service (max 20 years used for calculation)
• Weekly Pay is subject to the statutory cap.
• Age is at the effective date of termination.
- Employment Rights Act 1996, s.162 (Amount of a redundancy payment)
- Employment Rights Act 1996, s.227 (Limit on amount of week's pay)
- Employment Rights Act 1996, s.228 (Maximum number of years' service)
Procedural Fairness Checker
This is a practice tool for illustrative purposes only. It does not constitute legal advice. It provides a basic checklist for ACAS-compliant disciplinary procedures. Procedural fairness depends on the full circumstances of each case. Always consult a qualified professional for specific legal guidance.
ACAS Code Compliance Checklist
ACAS Guidance
Settlement Negotiation Planner
Plan a structured sequence of offers designed to reach your target settlement without splitting the difference.
This tool generates a disciplined offer sequence based on the Ackerman/Voss protocol. You set the target figure; the tool calculates when and how much to concede at each round. It does not constitute legal advice.
The Ackerman model avoids the common trap of "splitting the difference". Instead, you make a series of carefully planned offers, each one smaller than the last, so your opponent sees you approaching a firm limit. Enter your target settlement figure, choose your perspective (paying or receiving), and optionally enter the opponent's opening position to see where the two sides are likely to converge.
🤝 How the Offer Sequence Works
Convergence Plot: Shows how your offers move towards your target settlement. If you've entered the opponent's position, their projected concessions appear as a dashed line. Where the two lines meet is the likely settlement zone.
Negotiation Surface: A 3D visualisation of negotiation "tension". The saddle point (the flat middle) represents the equilibrium where neither side can improve their position without the other walking away — this is your target settlement.
Case Success Predictor
Structured assessment that balances four key factors — jurisdictional viability, legal strength, evidence quality, and historical baseline — to give a composite view of claim prospects.
The inputs require an informed legal practitioner. You assess each factor based on the specific facts; the tool weights and combines those assessments mathematically.
This tool structures professional judgment — it does not replace it. The factor scores you set must reflect your informed assessment of the specific case. It is not legal advice.
Calibration: Historical success rates are drawn from Employment Tribunal statistics (2020-21) and serve as a starting baseline. Your factor assessments then adjust that baseline up or down.
Is the claim in time? Is there tribunal jurisdiction? Consider ACAS early conciliation, extension arguments (just and equitable / not reasonably practicable), and whether the correct respondent is named.
How strong is the legal argument? Consider whether each element of the cause of action can be established, the quality of any comparator, and whether the respondent has viable defences (e.g., justification, fair reason).
How strong is the documentary and witness evidence? Consider contemporaneous records, credibility of witnesses, disclosure risks, and whether the burden of proof shifts (s.136 EqA).
How much should the general tribunal success rate for this claim type anchor the prediction? A low weight means your case-specific factors dominate; a high weight pulls toward the statistical average.
How It Works
The model starts from the historical tribunal success rate for the selected claim type, then adjusts up or down based on three case-specific factors you assess: jurisdiction, legal merits, and evidence quality.
Weights control how much each factor influences the overall prediction. They must total 100%. Adjusting one weight automatically redistributes the others.
The result is only as good as the assessments you put in. Two experienced practitioners may reasonably differ on factor scores — the tool makes that disagreement visible and structured rather than hidden in a gut feel.
📚 Mathematical Foundation: General Linear Model
This tool implements a multiple regression model, a special case of the General Linear Model (GLM). Following Olsson (2002), the observed outcome $y_i$ is modelled as a linear function of independent variables:
Our application: We adapt this framework for case success prediction, where the response variable is probability of success ($P_{\text{success}}$) and the predictors are normalised factor scores:
• $H$ = Historical baseline rate (intercept $\beta_0$)
• $w_i$ = Normalised weight for factor $i$
• $S_i$ = Strength score (0-100), centred at 50
• Subscripts: L=Legal, B=Burden, E=Evidence
💰 Next Step: Settlement Analysis
Model the financial settlement range and litigation risk using this success probability.
⚖️ Legal Element Analyser
Map the chain of legal elements needed for your claim and see how each assessment affects the overall probability.
This tool models how legal elements depend on each other. Each slider represents your professional assessment of whether a legal test is met. The network calculates the combined probability using Bayesian inference.
Path Dependency: These networks enforce sine qua non logic. Where a legal element is a prerequisite for downstream elements (e.g., disability must be established before "something arising" can exist under s.15 EqA 2010), the conditional probability is locked at 0%. Sliders marked as locked reflect legal impossibility, not mere improbability.
Employment claims require proving a chain of legal elements — each building on the last. For example, a s.15 disability discrimination claim needs: (1) a disability, (2) something arising from it, (3) unfavourable treatment, and (4) no justification. This tool lets you set your confidence on each element and instantly see the overall probability. Locked elements reflect legal impossibility: you cannot have 'something arising from disability' without first establishing a disability.
📚 Mathematical Foundation: Bayesian Networks
This tool utilizes Bayesian Networks (BNs), a powerful probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph (DAG).
• $X_i$ = A random variable (node) in the network
• Parents($X_i$) = The set of parent nodes directly influencing $X_i$
• $P(X_i | \text{Parents}(X_i))$ = Conditional Probability Distribution (CPD) for $X_i$ given its parents.
Inference in Bayesian Networks, the process of computing the posterior probability of a variable given evidence, is performed using algorithms like Variable Elimination or Message Passing.
• Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann.
• Koller, D., & Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press.
🔄 Evidence Updater
See how new evidence shifts your assessment of case strength or quantum.
Start with your initial assessment, then input the new evidence. The tool calculates a mathematically weighted update, giving more weight to whichever input you're more confident about.
Litigation assessments change as evidence emerges. A case you rated at 60% before disclosure might shift to 40% after seeing the documents — or to 80%. This tool formalises that process: enter your starting view and the new evidence, and see exactly how the updated assessment is calculated. Use Success Likelihood mode for probability of winning; use Quantum Estimate mode for damages values.
📚 Mathematical Foundation: Bayes-Laplace Theorem
This tool implements Bayesian inference, updating prior beliefs in light of new evidence using the Bayes-Laplace formula (Bayes, 1763; Laplace, 1774):
• $P(H)$ = Prior probability — your initial belief before seeing evidence
• $P(E \mid H)$ = Likelihood — probability of observing evidence $E$ if hypothesis $H$ is true
• $P(E)$ = Marginal likelihood — total probability of evidence under all hypotheses
• $P(H \mid E)$ = Posterior probability — updated belief after observing evidence
This tool's implementation: We model uncertainty using Beta distributions for probabilities and Normal distributions for monetary values. The posterior is computed via conjugate updating, which has closed-form solutions for these distribution families.
• Bayes, T. (1763). An Essay towards solving a Problem in the Doctrine of Chances. Phil. Trans. Royal Soc. 53:370-418.
• Laplace, P.S. (1774). Mémoire sur la probabilité des causes par les événemens. Mém. Acad. Roy. Sci. 6:621-656.
• Gelman, A. et al. (2013). Bayesian Data Analysis (3rd ed.). Chapman & Hall/CRC.
⚖️ Pension Loss Calculator
Calculate the pension element of compensation following the Principles for Compensating Pension Loss (4th Ed, 2021).
Pension loss calculations require actuarial understanding. This tool implements the judicial guidance methodology. Always verify against the full Ogden Tables and current discount rates.
Which method should you use? The Simple method is for defined contribution (DC) pensions — typically workplace auto-enrolment schemes where the employer pays a percentage of salary into a pot. You're calculating the lost contributions. The Complex method is for defined benefit (DB) pensions — final salary or career average schemes promising a specific annual pension. This requires Ogden Table multipliers and is significantly more involved. If the claimant was in a standard workplace pension (e.g. NEST, NOW: Pensions), use Simple. If they had a final salary or public sector scheme, use Complex.
For DC pensions, the loss is straightforward: the employer contributions that would have been paid into the pension pot during the loss period.
Annual Pension Values
Actuarial Factors (Ogden Tables)
Lump Sum Adjustments
⚖️ Settlement Range Modeller
Model the range of possible tribunal awards by running thousands of simulated outcomes.
Enter your best, worst, and most likely estimates for each head of loss. The tool runs thousands of simulated tribunals to show the probable range of total awards.
Rather than producing a single damages figure, this tool acknowledges uncertainty. For each head of loss, you provide three estimates: the minimum realistic award, the most likely award, and the maximum realistic award. The tool then simulates thousands of possible outcomes, sampling from these ranges, to build a picture of where the total award is likely to fall. This helps frame realistic settlement discussions.
📚 Mathematical Foundation: Monte Carlo & PERT
This tool models litigation uncertainty using Monte Carlo simulation. It performs thousands of trials, sampling from probability distributions for each head of loss to generate a range of potential outcomes.
By aggregating these distributions (e.g., Unfair Dismissal + Vento + Pension), the model reveals the probability density of the total award, helping to identify the most likely settlement range.
• Malcolm, D. G., et al. (1959). Application of a Technique for R&D Program Evaluation (PERT). Operations Research.
• Vose, D. (2008). Risk Analysis: A Quantitative Guide. John Wiley & Sons.
• Palisade Corporation. (2024). Guide to Monte Carlo Simulation.
Case Law Network
Interactive map of 500+ employment law authorities, clustered by topic.
This is an experimental educational tool for illustrative purposes only. It does not constitute legal advice. Clusters and connections represent topic shared between cases and are not a substitute for legal research. Always consult a qualified professional for specific legal guidance.